This document summarizes research on using different neurofeedback training techniques like EEG, HEG, and fMRI to enhance cognition. It discusses how these methods can be used to modify brain activity and regulate cognitive functions. Specifically, it describes EEG neurofeedback protocols that focus on brain waves like alpha, beta, theta, and gamma to improve areas like memory, concentration, stress levels. The document also reviews studies showing neurofeedback training can enhance executive functions and alter brain connectivity and activity related to attention, working memory, and other cognitive skills.
Keyseg: adaptive segmentation for spontaneous electroencephalography map seri...journalBEEI
Music is being studied related to either its impact on the psychological interaction or cognitive process behind it. These examinations bring out music's coordination to numerous disciplines including neuroscience. A few past examinations exhibited the contrast among musicians and non-musicians regarding brain structure and brain activity. The current investigation exhibited the diverse brain activation while musicians tuned in to music with regards to their musical experiences utilizing microstate classes method analysis. The investigation intended to determine electroencephalography microstate changes in Karawitan musicians' brain while tuning in to Gendhing Lancaran. Applying the electroencephalography microstate investigation of Karawitan musicians, the occurrence parameters was computed for four microstate classes (A, B, C, and D). Microstate properties were compared among subjects and correlated to Gendhing Lancaran perception. The present results revealed that Karawitan musicians' brain were characterized by microstate classes with the increased prominence of classes A, B, and D, but decreased prominence of classes C while tuning in to Gendhing Lancaran. Our finding is the first study to identify the typical microstate characteristics of the Karawitan musician’s brains while tuning in to Gendhing Lancaran by using the microstate segmentaion method.
This document discusses cognitive and emotional neuroprostheses and BCI research organizations and trends. It covers volitional prostheses that retrieve neuron activity related to action planning to control prosthetics. Memory prostheses address brain damage causing memory loss by replacing damaged hippocampal cells. Neurofeedback trains subjects to control brain activity using visual/auditory feedback. Major BCI research organizations including HUMAINE, BBCI and Neurobotics are described along with their goals of developing technologies related to emotion, BCI devices, and combining neuroscience and robotics.
This article is a review of neurofeedback techniques in the broad context of various clinical implications. Authors presented the neurophysiological background of these developing methods in relation to the state-of-the-art techniques. The broad range of methods of neurofeedback were reviewed, comprising the transfer of information, automation, brain-computer interface, multichannel Z-Score neurofeedback and slow cortical potentials. Neurofeedback may be an effective tool for self-regulation, useful for achieving better selfknowledge and enhanced cognitive skills. A tailored, dedicated program, based on quantitative electroencephalographic (QEEG) assessment and/or Z-Score should be implemented for a given patient in order to gain trust and fulfill the compliance. The proven clinical benefits of multi-channel neurofeedback, targeting regulation of particular brain regions, or inducing specific neural patterns, may be an alternative method for treating diseases in a non-invasive, introspective way. Effective modulation of the physiological functions which may affect various neural mechanisms of cognition and behavior seems to be the future perspective of neurofeedback
TRANSDISCIPLINARY APPROACH TO SPEECH THERAPY WITH CHILDREN WITH ALALIASubmissionResearchpa
The article discloses the practical side of the transdisciplinary approach in speech therapy work and rehabilitation of children with alalia. In diagnosing speech disorders, the author believes that the behavior of children with alalia is closely related to the physiological activity of the brain, that diagnosis of speech disorders based on EEG and neuropsychological methods, as well as knowledge of the insufficiency of individual brain structures, which is supported by data from electroencephalographic research, are important for speech therapists in complex rehabilitation. Binaural therapy (individual alpha balance correction program) was used for children after EEG diagnosis of their dominant alpha rhythm, which matched their dominant rhythm in the selected time interval by Karimova Shoira Tursunovna 2020. TRANSDISCIPLINARY APPROACH TO SPEECH THERAPY WITH CHILDREN WITH ALALIA. International Journal on Integrated Education. 3, 8 (Sep. 2020), 238-241. DOI:https://doi.org/10.31149/ijie.v3i8.574 https://journals.researchparks.org/index.php/IJIE/article/view/574/549 https://journals.researchparks.org/index.php/IJIE/article/view/574
More advanced thinkers like Eleanor A. Maguire, a professor of Cognitive Neuroscience
at the University of London mentions in her study of taxi drivers that the hippocampus is
the seat of spatial reasoning, memory planning for the future and is located in
posterior hippocampus-the spatial processing center.
This document provides summaries of four keynote presentations and four symposia abstracts from the Australasian Cognitive Neurosciences Conference held in December 2012.
The keynote presentations discuss research on how memory supports future thinking, limits of subliminal processing and signatures of conscious access, temporal processing deficits in neurodevelopmental disorders, and the role of neural oscillations in schizophrenia and brain development.
The symposia abstracts report on research examining individual differences in cognitive flexibility and its neural bases, the neural mechanisms underlying cognitive control over reward in opiate users and healthy controls, behavioral control in adults with schizophrenia and children at elevated risk, and the development of cognitive control networks from childhood to adulthood.
Pachygyria a neurological migration disorderpharmaindexing
Pachygyria is a rare neurological migration disorder characterized by thick convolutions on the cerebral cortex resulting in fewer gyri. It is caused by defects in neuronal migration during fetal development. Patients with pachygyria typically experience seizures, developmental delays, and intellectual disabilities. MRI and CT scans are used to diagnose pachygyria by identifying thickened cerebral cortices with few large gyri. While there is no cure, treatment focuses on managing seizures, providing therapies to address delays, and being supportive of overall development.
The Connection between Meditation and Creativity (Main paper)Olatundun Makinde
This document discusses the neurophysiological mechanisms that reveal the connection between meditation and creativity. Several studies have found increases in grey matter concentration and cortical thickness in brain regions involved in attention, sensory processing, and introspection in experienced meditators. Brain imaging studies show regular meditators require less brain activation to perform attention-based tasks compared to non-meditators. EEG studies demonstrate meditation shifts brain activity towards an optimal state associated with improved cognitive flexibility, working memory, and executive function. Together, these findings suggest meditation can enhance brain efficiency and structure in ways that may increase creativity.
Keyseg: adaptive segmentation for spontaneous electroencephalography map seri...journalBEEI
Music is being studied related to either its impact on the psychological interaction or cognitive process behind it. These examinations bring out music's coordination to numerous disciplines including neuroscience. A few past examinations exhibited the contrast among musicians and non-musicians regarding brain structure and brain activity. The current investigation exhibited the diverse brain activation while musicians tuned in to music with regards to their musical experiences utilizing microstate classes method analysis. The investigation intended to determine electroencephalography microstate changes in Karawitan musicians' brain while tuning in to Gendhing Lancaran. Applying the electroencephalography microstate investigation of Karawitan musicians, the occurrence parameters was computed for four microstate classes (A, B, C, and D). Microstate properties were compared among subjects and correlated to Gendhing Lancaran perception. The present results revealed that Karawitan musicians' brain were characterized by microstate classes with the increased prominence of classes A, B, and D, but decreased prominence of classes C while tuning in to Gendhing Lancaran. Our finding is the first study to identify the typical microstate characteristics of the Karawitan musician’s brains while tuning in to Gendhing Lancaran by using the microstate segmentaion method.
This document discusses cognitive and emotional neuroprostheses and BCI research organizations and trends. It covers volitional prostheses that retrieve neuron activity related to action planning to control prosthetics. Memory prostheses address brain damage causing memory loss by replacing damaged hippocampal cells. Neurofeedback trains subjects to control brain activity using visual/auditory feedback. Major BCI research organizations including HUMAINE, BBCI and Neurobotics are described along with their goals of developing technologies related to emotion, BCI devices, and combining neuroscience and robotics.
This article is a review of neurofeedback techniques in the broad context of various clinical implications. Authors presented the neurophysiological background of these developing methods in relation to the state-of-the-art techniques. The broad range of methods of neurofeedback were reviewed, comprising the transfer of information, automation, brain-computer interface, multichannel Z-Score neurofeedback and slow cortical potentials. Neurofeedback may be an effective tool for self-regulation, useful for achieving better selfknowledge and enhanced cognitive skills. A tailored, dedicated program, based on quantitative electroencephalographic (QEEG) assessment and/or Z-Score should be implemented for a given patient in order to gain trust and fulfill the compliance. The proven clinical benefits of multi-channel neurofeedback, targeting regulation of particular brain regions, or inducing specific neural patterns, may be an alternative method for treating diseases in a non-invasive, introspective way. Effective modulation of the physiological functions which may affect various neural mechanisms of cognition and behavior seems to be the future perspective of neurofeedback
TRANSDISCIPLINARY APPROACH TO SPEECH THERAPY WITH CHILDREN WITH ALALIASubmissionResearchpa
The article discloses the practical side of the transdisciplinary approach in speech therapy work and rehabilitation of children with alalia. In diagnosing speech disorders, the author believes that the behavior of children with alalia is closely related to the physiological activity of the brain, that diagnosis of speech disorders based on EEG and neuropsychological methods, as well as knowledge of the insufficiency of individual brain structures, which is supported by data from electroencephalographic research, are important for speech therapists in complex rehabilitation. Binaural therapy (individual alpha balance correction program) was used for children after EEG diagnosis of their dominant alpha rhythm, which matched their dominant rhythm in the selected time interval by Karimova Shoira Tursunovna 2020. TRANSDISCIPLINARY APPROACH TO SPEECH THERAPY WITH CHILDREN WITH ALALIA. International Journal on Integrated Education. 3, 8 (Sep. 2020), 238-241. DOI:https://doi.org/10.31149/ijie.v3i8.574 https://journals.researchparks.org/index.php/IJIE/article/view/574/549 https://journals.researchparks.org/index.php/IJIE/article/view/574
More advanced thinkers like Eleanor A. Maguire, a professor of Cognitive Neuroscience
at the University of London mentions in her study of taxi drivers that the hippocampus is
the seat of spatial reasoning, memory planning for the future and is located in
posterior hippocampus-the spatial processing center.
This document provides summaries of four keynote presentations and four symposia abstracts from the Australasian Cognitive Neurosciences Conference held in December 2012.
The keynote presentations discuss research on how memory supports future thinking, limits of subliminal processing and signatures of conscious access, temporal processing deficits in neurodevelopmental disorders, and the role of neural oscillations in schizophrenia and brain development.
The symposia abstracts report on research examining individual differences in cognitive flexibility and its neural bases, the neural mechanisms underlying cognitive control over reward in opiate users and healthy controls, behavioral control in adults with schizophrenia and children at elevated risk, and the development of cognitive control networks from childhood to adulthood.
Pachygyria a neurological migration disorderpharmaindexing
Pachygyria is a rare neurological migration disorder characterized by thick convolutions on the cerebral cortex resulting in fewer gyri. It is caused by defects in neuronal migration during fetal development. Patients with pachygyria typically experience seizures, developmental delays, and intellectual disabilities. MRI and CT scans are used to diagnose pachygyria by identifying thickened cerebral cortices with few large gyri. While there is no cure, treatment focuses on managing seizures, providing therapies to address delays, and being supportive of overall development.
The Connection between Meditation and Creativity (Main paper)Olatundun Makinde
This document discusses the neurophysiological mechanisms that reveal the connection between meditation and creativity. Several studies have found increases in grey matter concentration and cortical thickness in brain regions involved in attention, sensory processing, and introspection in experienced meditators. Brain imaging studies show regular meditators require less brain activation to perform attention-based tasks compared to non-meditators. EEG studies demonstrate meditation shifts brain activity towards an optimal state associated with improved cognitive flexibility, working memory, and executive function. Together, these findings suggest meditation can enhance brain efficiency and structure in ways that may increase creativity.
Role of mirror therapy in neurological conditionsRuchika Gupta
This document provides a literature review on the role of mirror therapy in neurological conditions. It summarizes 14 studies that examine the effectiveness of mirror therapy for various conditions such as stroke, phantom limb pain, complex regional pain syndrome, cerebral palsy, and Parkinson's disease. The studies generally found that mirror therapy improved motor function, decreased pain, and enhanced cortical excitability when used individually or as an adjunct to other therapies. The review concludes that mirror therapy is a promising treatment approach for re-educating the brain and stimulating mirror neurons to improve motor control and recovery from neurological impairments.
Neurological symptoms are caused by issues in the nervous system, which consists of the central nervous system (brain and spinal cord) and peripheral nervous system. A neurologist is a physician who specializes in diagnosing and treating disorders of the nervous system. They examine patients, order tests like MRI and EEG to evaluate the nervous system, diagnose conditions, develop treatment plans, and monitor patient progress. Some neurologists specialize in areas like headaches, tumors, development issues, or vascular disorders of the brain.
An Efficacy Study on Improving Balance in Subacute Stroke Patients by Proprio...ijtsrd
INTRODUCTION CVA is a complex dysfunction caused by a lesion in the brain. The WHO defines stroke as an “acute neurologic dysfunction of vascular origin with symptoms and sign corresponding to the involvement of focal areas of the brain.” In India the cumulative incidence of stroke ranged from 105 152 100000 persons per year, and the crude prevalence of stroke ranged from 44.29 559 100000 persons in different parts of the country during the past decade. DESIGN Pre test Post test experimental group designSETTING Inpatient and outpatient of Department of Occupational Therapy, SV.NIRTAR, Olatpur.PARTICIPANTS A total 45 Subjects were recruited from the Swami Vivekananda National Institute of Rehabilitation Training and Research according to the inclusion and exclusion criteria.INTERVENTIONS After meeting the inclusion and exclusion criteria survivors were assessed using assessment performance, and informed consent was taken from the participants, allocated to the three groups.Group 1 Proprioceptive training alone Group 2 Proprioceptive training along with motor imageryGroup 3 Conventional therapyOUTCOME MEASURES Berg Balance Scale RESULT The study aimed to provide reference data for planning the rehabilitation of stroke patients, by comparing the effects of proprioceptive training with motor imagery and conventional proprioceptive training performed for 8 weeks. Result of the study indicated that there was significant effect of mental imagery and proprioceptive training on balance ability of stroke patients. The changes of the motor imagery training group were better than those of the other 2 groups.CONCLUSION In this clinical trial, our findings suggests significant improvement in balance in sub acute stroke patients when given motor imagery training along with proprioceptive training, conventional therapy and proprioceptive training alone.On the basis of current results, it was also concluded that, the motor imagery training along with proprioceptive training group showed a noticeable better effect on balance than those of other two groups. Kshanaprava Dash | Mr. Rama Kumar Sahu "An Efficacy Study on Improving Balance in Subacute Stroke Patients by Proprioceptive Training with Additional Motor Imagery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38382.pdf Paper Url: https://www.ijtsrd.com/medicine/other/38382/an-efficacy-study-on-improving-balance-in-subacute-stroke-patients-by-proprioceptive-training-with-additional-motor-imagery/kshanaprava-dash
Cognitive rehabilitation aims to help recover mental functions impaired by brain injury through restoration, substitution, and restructuring approaches. Computerized cognitive rehabilitation (CACR) uses computer programs and exercises to retrain impaired cognitive skills. It provides personalized feedback and reinforcement through tasks of increasing difficulty. Research shows CACR improves attention, memory, and executive functions in patients with brain injuries, learning disabilities, schizophrenia, substance abuse disorders, depression, and multiple sclerosis compared to control groups. CACR appears as effective as traditional face-to-face rehabilitation with similar costs. Issues include variability in brain injury characteristics and rehabilitation programs across studies.
This document discusses cognitive rehabilitation therapy (CRT). It began in the 1920s to help veterans with brain injuries relearn cognitive skills. CRT aims to restore lost cognitive functions or teach compensatory strategies. An interdisciplinary team may provide CRT to help those with conditions like stroke, dementia, TBI regain independence. Occupational therapists play a key role in CRT by helping clients relearn skills through functional activities to improve daily living. Strong evidence shows CRT's effectiveness, especially when provided through interdisciplinary collaboration.
Pacitane nanoparticles drug release for brain disorderIJSRED
This document summarizes research on using nanoparticles to deliver drugs to the brain and treat brain disorders. It discusses how nanoparticles carrying drugs can be administered orally and pass through the gastrointestinal tract and bloodstream to reach the brain. Specifically, it describes an experiment where pacitane nanoparticles were prepared and tested as a potential delivery system for treating brain disorders like Parkinson's disease and schizophrenia. The nanoparticles were characterized and tested for drug release in vitro. The results showed 80% of the drug was released within 24 hours. The conclusion is that nanoparticle drug delivery could provide an effective therapy for brain disorders.
Autism challenges the individual, their family, the community and the practitioner. The complexity and variety of symptoms that define Autism Spectrum Disorders (ASD) require service providers to constantly update their knowledge and skills to best serve these individuals. Sensorimotor synchronization training with Interactive Metronome (IM), as part of a comprehensive treatment plan, has the potential to directly and positively influence the person’s ability to learn and engage with the world around him. This course will introduce practitioners to new advances in our understanding of ASD, including the pivotal role of timing & rhythm for speech, language, pragmatic, cognitive, motor and sensory processing skills. Participants will learn strategies to evaluate the unique strengths and needs of each individual with ASD as well as how to develop and implement effective IM training with consideration for communication, sensory, motor & behavioral challenges. The expert presenter will incorporate several videos and real examples to illustrate techniques. Take the course at https://secure.interactivemetronome.com/NetSite/IM/CEU/SimpleRegistration.aspx?course_id=2778
Interventions to Improve Cognitive Functioning After TBILoki Stormbringer
This document discusses interventions to improve cognitive functioning after traumatic brain injury (TBI). It begins by introducing a case study of a veteran experiencing cognitive issues after multiple blast exposures during deployment. It then provides an overview of TBI, noting that while injuries are acute, cognitive deficits can persist chronically and impact individuals, families, and society. It discusses the importance of recognizing and treating chronic cognitive dysfunction, and how a combination of physical and psychological trauma from combat experiences could result in a complex "combined combat neurotrauma syndrome." The document advocates considering multiple levels of brain functioning and integrating behavioral and pharmacological therapies to effectively improve post-TBI cognitive functioning.
Correlation of learning disabilities to porn addiction based on EEGjournalBEEI
Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.
This document discusses EEG biofeedback, also known as neurofeedback. It explains that brainwaves can be measured by frequency and each frequency relates to different mental states. EEG biofeedback works by using sensors on the head to monitor brainwaves and providing feedback, like sounds or games, to train the brain to increase frequencies related to focus and relaxation. This helps rewire neural connections. The document notes studies showing EEG biofeedback can help addiction recovery by improving retention rates and long-term sobriety when used along with other treatments. It provides contact information for learning more.
EEG signal processing and application to Neurofeedback : Operant Conditioning...MOK wahedi
Introduce therapeutic applications of EEG & QEEG in health and disease
Know about Neurofeedback an emerging modality of treatment of disease where Drugs and surgery has limited effects.
Overview Neurofeedback World Market size and Bangladesh Context
Identify role of Engineers in the application of Neurofeedback and other Advanced Medical Brain imaging and Therapeutics.
EEG stands for “electroencephalography” which is an electrophysiological process to record the electrical activity of the brain.
An electroencephalogram (EEG) is a recording of brain activity.
EEG measures changes in the electrical activity produced by the brain.
Voltage changes come from ionic current within and between brain cells called neurons.
The electrodes of an EEG device capture electrical activity expressed in various EEG frequencies using an algorithm called a Fast Fourier Transform (FFT), these raw EEG signals can be identified as distinct waves with different frequencies.
Frequency, which refers to the speed of the electrical oscillations, is measured in cycles per second — one Hertz (Hz) is equal to one cycle per second.
Brainwaves are categorized by frequency into four main types: Beta, Alpha, Theta and Delta.
Quantitative
igital processing of EEG signals consists of different components: signal acquisition unit, feature extraction unit, and a decision algorithm.
The input to the system is an EEG signal acquired from the scalp, brain surface, or brain interior. The signal acquisition unit is represented by electrodes whether they are invasive or non-invasive.
The feature extraction unit is a signal processing unit aiming to extract discriminative features from channel(s).
The decision unit, in brain computer interface (BCI) for example, is a hybrid unit with the purpose of classification, decision-making, and passing the decisions to external devices outputting the intention of the subject.
Electroencephalography (qEEG) is a procedure that processes the recorded EEG activity from a multi-electrode recording using a computer.
This multi-channel EEG data is processed with various algorithms, such as the “Fourier” classically, or in more modern applications “Wavelet” analysis.
The digital data is statistically analyzed, sometimes comparing values with “normative” database reference values.
The processed EEG is commonly converted into color maps of brain functioning called “Brain maps”.
Quantitative EEG (qEEG) is the analysis of the digitized EEG, and in lay terms this sometimes is also called “Brain Mapping”.
In Neurofeedback (NF) the current parameters of EEG recorded from a subject’s head is presented to the subject through visual, auditory, or tactile modality, where the subject alters these parameters to reach a more efficient mode of brain functioning.
Over the last 40 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts.
Neurofeedback as Treatment of Autism Spectrum Disorder-MOK wahedi
ABSTRACT
Background
Autism Spectrum Disorder is a neurodevelopmental disorder and symptoms are considered to be due to brain deregulation and dysfunction in multiple regions of the brain. There is no cure for autism and the management guidelines are defined to reduce the symptoms and reinstitute normal functioning in life. Neurofeedback is one of these methods in treating children with Autism. Several studies have shown that individuals with autism are able to alter their brain activity in specific frequency bands through the use of neurofeedback which induces prolong changes in autism symptoms, cognitive functioning and long-term changes in EEG. Neurofeedback is a way of training the brain to use more productive patterns of brainwaves which teaches the brain to self-regulate, strengthening neural pathways while increasing mental endurance and flexibility
Objective
To observe the effect of Neurofeedback Treatment in Children with ASD in areas of speech, language, communication, sociability, sensory ,cognitive awareness, health, physical and behaviors
Methods and Materials
It is a descriptive longitudinal study with purposive sampling done from January 2018 to March 2020 among children diagnosed with ASD who underwent Neurofeedback training for at least 80 sessions. 70% were boys 30% were girls. Assessment was done by Autism Treatment Evaluation Checklist. Data were analyzed by SPSS 21
Result
The study showed Improvements in all ATEC Evaluation Categories in Speech, Language, and communication39.1%, Sociability53.7%, Sensory and Cognitive awareness56.8%, and in Health, Physical, and Behaviors58%
Conclusion
The study shows marked improvement in all 4 specific Areas of evaluation of ATEC after Neurofeedback training of children with Autism. Since Autism has no specific cure and neurofeedback has its efficacy without any untoward effects may be used as a treatment option for Autism
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
An Approach to Improve Brain Disorder Using Machine Learning TechniquesIOSR Journals
This summary provides an overview of an approach to improve diagnosis of attention deficit hyperactivity disorder (ADHD) using machine learning techniques. The study applies various machine learning algorithms like support vector machines (SVM), K-nearest neighbors (KNN), and co-training to electroencephalography (EEG) signals from patients in order to better classify and diagnose ADHD. MATLAB is used to analyze the EEG data and evaluate the performance of different machine learning methods at decomposing the signals, selecting relevant features, and determining the most effective approach for addressing brain disorders like ADHD. The goal is to develop a decision support system that can more accurately diagnose ADHD through analysis of brain activity patterns in EEG data using supervised and semi-supervised machine learning
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
This document summarizes a research paper that proposes an algorithm to predict epileptic seizures up to 5 minutes in advance using EEG data. The algorithm analyzes EEG recordings to extract seizure prediction characteristics and correlates these with seizure occurrence times. The algorithm could enable preventative therapies by triggering deep brain stimulation to avoid imminent seizures. When tested on 21 patients, the algorithm achieved 81.7% accuracy in predicting seizures up to 5 minutes beforehand, ranging from 80.7-81.5% accuracy for individual brain channels. If validated, this type of advanced seizure prediction could help minimize injuries from sudden seizures.
The document discusses various current applications of electroencephalography (EEG) technology both within and outside of clinical settings. It outlines EEG's predominant use in epilepsy and sleep disorder diagnosis clinically. It also explores recent developments that enable portable and cheaper EEG units, allowing novel consumer and research applications. Specifically, the document examines EEG's role in investigating sleep disorders, assessing brain death, monitoring anesthesia depth, cognitive engagement, brain development, and more. It explores EEG's growing use in cognitive science, neuroscience, and other research domains. Finally, it discusses emerging areas like brain-computer interfaces, closed-loop systems, and neuromarketing.
Neuromodulation is one of the newest and most promising brain-related medical techniques. Eyad Kishawi is an expert in neuromodulation and has experience in deep brain stimulation. Technically speaking, neuromodulation is the way that neurotransmitters in the brain react with each other to affect people’s mental and physical behaviors. Read below to learn more about the basics of neuromodulation.
Machine learning applications in clinical brain computer interfacingJenny Midwinter
Brain computer interfaces (BCIs) provide alternative communication channels from the brain to external devices for severely disabled individuals and can be used to induce and guide adaptive plasticity for recovery after central nervous system trauma. Clinical BCI effectiveness depends on robust and accurate modeling of the relationship between brain signals and behaviour. Dr. Boulay will give a brief survey of BCI technologies and discuss common BCI paradigms and implementations, with an emphasis on clinical BCI brain signals and machine-learning applications.
* From the Ottawa AI/ML Meetup June 2018.
Role of mirror therapy in neurological conditionsRuchika Gupta
This document provides a literature review on the role of mirror therapy in neurological conditions. It summarizes 14 studies that examine the effectiveness of mirror therapy for various conditions such as stroke, phantom limb pain, complex regional pain syndrome, cerebral palsy, and Parkinson's disease. The studies generally found that mirror therapy improved motor function, decreased pain, and enhanced cortical excitability when used individually or as an adjunct to other therapies. The review concludes that mirror therapy is a promising treatment approach for re-educating the brain and stimulating mirror neurons to improve motor control and recovery from neurological impairments.
Neurological symptoms are caused by issues in the nervous system, which consists of the central nervous system (brain and spinal cord) and peripheral nervous system. A neurologist is a physician who specializes in diagnosing and treating disorders of the nervous system. They examine patients, order tests like MRI and EEG to evaluate the nervous system, diagnose conditions, develop treatment plans, and monitor patient progress. Some neurologists specialize in areas like headaches, tumors, development issues, or vascular disorders of the brain.
An Efficacy Study on Improving Balance in Subacute Stroke Patients by Proprio...ijtsrd
INTRODUCTION CVA is a complex dysfunction caused by a lesion in the brain. The WHO defines stroke as an “acute neurologic dysfunction of vascular origin with symptoms and sign corresponding to the involvement of focal areas of the brain.” In India the cumulative incidence of stroke ranged from 105 152 100000 persons per year, and the crude prevalence of stroke ranged from 44.29 559 100000 persons in different parts of the country during the past decade. DESIGN Pre test Post test experimental group designSETTING Inpatient and outpatient of Department of Occupational Therapy, SV.NIRTAR, Olatpur.PARTICIPANTS A total 45 Subjects were recruited from the Swami Vivekananda National Institute of Rehabilitation Training and Research according to the inclusion and exclusion criteria.INTERVENTIONS After meeting the inclusion and exclusion criteria survivors were assessed using assessment performance, and informed consent was taken from the participants, allocated to the three groups.Group 1 Proprioceptive training alone Group 2 Proprioceptive training along with motor imageryGroup 3 Conventional therapyOUTCOME MEASURES Berg Balance Scale RESULT The study aimed to provide reference data for planning the rehabilitation of stroke patients, by comparing the effects of proprioceptive training with motor imagery and conventional proprioceptive training performed for 8 weeks. Result of the study indicated that there was significant effect of mental imagery and proprioceptive training on balance ability of stroke patients. The changes of the motor imagery training group were better than those of the other 2 groups.CONCLUSION In this clinical trial, our findings suggests significant improvement in balance in sub acute stroke patients when given motor imagery training along with proprioceptive training, conventional therapy and proprioceptive training alone.On the basis of current results, it was also concluded that, the motor imagery training along with proprioceptive training group showed a noticeable better effect on balance than those of other two groups. Kshanaprava Dash | Mr. Rama Kumar Sahu "An Efficacy Study on Improving Balance in Subacute Stroke Patients by Proprioceptive Training with Additional Motor Imagery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38382.pdf Paper Url: https://www.ijtsrd.com/medicine/other/38382/an-efficacy-study-on-improving-balance-in-subacute-stroke-patients-by-proprioceptive-training-with-additional-motor-imagery/kshanaprava-dash
Cognitive rehabilitation aims to help recover mental functions impaired by brain injury through restoration, substitution, and restructuring approaches. Computerized cognitive rehabilitation (CACR) uses computer programs and exercises to retrain impaired cognitive skills. It provides personalized feedback and reinforcement through tasks of increasing difficulty. Research shows CACR improves attention, memory, and executive functions in patients with brain injuries, learning disabilities, schizophrenia, substance abuse disorders, depression, and multiple sclerosis compared to control groups. CACR appears as effective as traditional face-to-face rehabilitation with similar costs. Issues include variability in brain injury characteristics and rehabilitation programs across studies.
This document discusses cognitive rehabilitation therapy (CRT). It began in the 1920s to help veterans with brain injuries relearn cognitive skills. CRT aims to restore lost cognitive functions or teach compensatory strategies. An interdisciplinary team may provide CRT to help those with conditions like stroke, dementia, TBI regain independence. Occupational therapists play a key role in CRT by helping clients relearn skills through functional activities to improve daily living. Strong evidence shows CRT's effectiveness, especially when provided through interdisciplinary collaboration.
Pacitane nanoparticles drug release for brain disorderIJSRED
This document summarizes research on using nanoparticles to deliver drugs to the brain and treat brain disorders. It discusses how nanoparticles carrying drugs can be administered orally and pass through the gastrointestinal tract and bloodstream to reach the brain. Specifically, it describes an experiment where pacitane nanoparticles were prepared and tested as a potential delivery system for treating brain disorders like Parkinson's disease and schizophrenia. The nanoparticles were characterized and tested for drug release in vitro. The results showed 80% of the drug was released within 24 hours. The conclusion is that nanoparticle drug delivery could provide an effective therapy for brain disorders.
Autism challenges the individual, their family, the community and the practitioner. The complexity and variety of symptoms that define Autism Spectrum Disorders (ASD) require service providers to constantly update their knowledge and skills to best serve these individuals. Sensorimotor synchronization training with Interactive Metronome (IM), as part of a comprehensive treatment plan, has the potential to directly and positively influence the person’s ability to learn and engage with the world around him. This course will introduce practitioners to new advances in our understanding of ASD, including the pivotal role of timing & rhythm for speech, language, pragmatic, cognitive, motor and sensory processing skills. Participants will learn strategies to evaluate the unique strengths and needs of each individual with ASD as well as how to develop and implement effective IM training with consideration for communication, sensory, motor & behavioral challenges. The expert presenter will incorporate several videos and real examples to illustrate techniques. Take the course at https://secure.interactivemetronome.com/NetSite/IM/CEU/SimpleRegistration.aspx?course_id=2778
Interventions to Improve Cognitive Functioning After TBILoki Stormbringer
This document discusses interventions to improve cognitive functioning after traumatic brain injury (TBI). It begins by introducing a case study of a veteran experiencing cognitive issues after multiple blast exposures during deployment. It then provides an overview of TBI, noting that while injuries are acute, cognitive deficits can persist chronically and impact individuals, families, and society. It discusses the importance of recognizing and treating chronic cognitive dysfunction, and how a combination of physical and psychological trauma from combat experiences could result in a complex "combined combat neurotrauma syndrome." The document advocates considering multiple levels of brain functioning and integrating behavioral and pharmacological therapies to effectively improve post-TBI cognitive functioning.
Correlation of learning disabilities to porn addiction based on EEGjournalBEEI
Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.
This document discusses EEG biofeedback, also known as neurofeedback. It explains that brainwaves can be measured by frequency and each frequency relates to different mental states. EEG biofeedback works by using sensors on the head to monitor brainwaves and providing feedback, like sounds or games, to train the brain to increase frequencies related to focus and relaxation. This helps rewire neural connections. The document notes studies showing EEG biofeedback can help addiction recovery by improving retention rates and long-term sobriety when used along with other treatments. It provides contact information for learning more.
EEG signal processing and application to Neurofeedback : Operant Conditioning...MOK wahedi
Introduce therapeutic applications of EEG & QEEG in health and disease
Know about Neurofeedback an emerging modality of treatment of disease where Drugs and surgery has limited effects.
Overview Neurofeedback World Market size and Bangladesh Context
Identify role of Engineers in the application of Neurofeedback and other Advanced Medical Brain imaging and Therapeutics.
EEG stands for “electroencephalography” which is an electrophysiological process to record the electrical activity of the brain.
An electroencephalogram (EEG) is a recording of brain activity.
EEG measures changes in the electrical activity produced by the brain.
Voltage changes come from ionic current within and between brain cells called neurons.
The electrodes of an EEG device capture electrical activity expressed in various EEG frequencies using an algorithm called a Fast Fourier Transform (FFT), these raw EEG signals can be identified as distinct waves with different frequencies.
Frequency, which refers to the speed of the electrical oscillations, is measured in cycles per second — one Hertz (Hz) is equal to one cycle per second.
Brainwaves are categorized by frequency into four main types: Beta, Alpha, Theta and Delta.
Quantitative
igital processing of EEG signals consists of different components: signal acquisition unit, feature extraction unit, and a decision algorithm.
The input to the system is an EEG signal acquired from the scalp, brain surface, or brain interior. The signal acquisition unit is represented by electrodes whether they are invasive or non-invasive.
The feature extraction unit is a signal processing unit aiming to extract discriminative features from channel(s).
The decision unit, in brain computer interface (BCI) for example, is a hybrid unit with the purpose of classification, decision-making, and passing the decisions to external devices outputting the intention of the subject.
Electroencephalography (qEEG) is a procedure that processes the recorded EEG activity from a multi-electrode recording using a computer.
This multi-channel EEG data is processed with various algorithms, such as the “Fourier” classically, or in more modern applications “Wavelet” analysis.
The digital data is statistically analyzed, sometimes comparing values with “normative” database reference values.
The processed EEG is commonly converted into color maps of brain functioning called “Brain maps”.
Quantitative EEG (qEEG) is the analysis of the digitized EEG, and in lay terms this sometimes is also called “Brain Mapping”.
In Neurofeedback (NF) the current parameters of EEG recorded from a subject’s head is presented to the subject through visual, auditory, or tactile modality, where the subject alters these parameters to reach a more efficient mode of brain functioning.
Over the last 40 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts.
Neurofeedback as Treatment of Autism Spectrum Disorder-MOK wahedi
ABSTRACT
Background
Autism Spectrum Disorder is a neurodevelopmental disorder and symptoms are considered to be due to brain deregulation and dysfunction in multiple regions of the brain. There is no cure for autism and the management guidelines are defined to reduce the symptoms and reinstitute normal functioning in life. Neurofeedback is one of these methods in treating children with Autism. Several studies have shown that individuals with autism are able to alter their brain activity in specific frequency bands through the use of neurofeedback which induces prolong changes in autism symptoms, cognitive functioning and long-term changes in EEG. Neurofeedback is a way of training the brain to use more productive patterns of brainwaves which teaches the brain to self-regulate, strengthening neural pathways while increasing mental endurance and flexibility
Objective
To observe the effect of Neurofeedback Treatment in Children with ASD in areas of speech, language, communication, sociability, sensory ,cognitive awareness, health, physical and behaviors
Methods and Materials
It is a descriptive longitudinal study with purposive sampling done from January 2018 to March 2020 among children diagnosed with ASD who underwent Neurofeedback training for at least 80 sessions. 70% were boys 30% were girls. Assessment was done by Autism Treatment Evaluation Checklist. Data were analyzed by SPSS 21
Result
The study showed Improvements in all ATEC Evaluation Categories in Speech, Language, and communication39.1%, Sociability53.7%, Sensory and Cognitive awareness56.8%, and in Health, Physical, and Behaviors58%
Conclusion
The study shows marked improvement in all 4 specific Areas of evaluation of ATEC after Neurofeedback training of children with Autism. Since Autism has no specific cure and neurofeedback has its efficacy without any untoward effects may be used as a treatment option for Autism
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
An Approach to Improve Brain Disorder Using Machine Learning TechniquesIOSR Journals
This summary provides an overview of an approach to improve diagnosis of attention deficit hyperactivity disorder (ADHD) using machine learning techniques. The study applies various machine learning algorithms like support vector machines (SVM), K-nearest neighbors (KNN), and co-training to electroencephalography (EEG) signals from patients in order to better classify and diagnose ADHD. MATLAB is used to analyze the EEG data and evaluate the performance of different machine learning methods at decomposing the signals, selecting relevant features, and determining the most effective approach for addressing brain disorders like ADHD. The goal is to develop a decision support system that can more accurately diagnose ADHD through analysis of brain activity patterns in EEG data using supervised and semi-supervised machine learning
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
This document summarizes a research paper that proposes an algorithm to predict epileptic seizures up to 5 minutes in advance using EEG data. The algorithm analyzes EEG recordings to extract seizure prediction characteristics and correlates these with seizure occurrence times. The algorithm could enable preventative therapies by triggering deep brain stimulation to avoid imminent seizures. When tested on 21 patients, the algorithm achieved 81.7% accuracy in predicting seizures up to 5 minutes beforehand, ranging from 80.7-81.5% accuracy for individual brain channels. If validated, this type of advanced seizure prediction could help minimize injuries from sudden seizures.
The document discusses various current applications of electroencephalography (EEG) technology both within and outside of clinical settings. It outlines EEG's predominant use in epilepsy and sleep disorder diagnosis clinically. It also explores recent developments that enable portable and cheaper EEG units, allowing novel consumer and research applications. Specifically, the document examines EEG's role in investigating sleep disorders, assessing brain death, monitoring anesthesia depth, cognitive engagement, brain development, and more. It explores EEG's growing use in cognitive science, neuroscience, and other research domains. Finally, it discusses emerging areas like brain-computer interfaces, closed-loop systems, and neuromarketing.
Neuromodulation is one of the newest and most promising brain-related medical techniques. Eyad Kishawi is an expert in neuromodulation and has experience in deep brain stimulation. Technically speaking, neuromodulation is the way that neurotransmitters in the brain react with each other to affect people’s mental and physical behaviors. Read below to learn more about the basics of neuromodulation.
Machine learning applications in clinical brain computer interfacingJenny Midwinter
Brain computer interfaces (BCIs) provide alternative communication channels from the brain to external devices for severely disabled individuals and can be used to induce and guide adaptive plasticity for recovery after central nervous system trauma. Clinical BCI effectiveness depends on robust and accurate modeling of the relationship between brain signals and behaviour. Dr. Boulay will give a brief survey of BCI technologies and discuss common BCI paradigms and implementations, with an emphasis on clinical BCI brain signals and machine-learning applications.
* From the Ottawa AI/ML Meetup June 2018.
The teacher asked the student which specific cognitive functions they would like to use brain oscillatory analysis to better understand. The student responded that they are interested in using it to study visual short-term memory and how oscillations change when mistakes are made on visual memory tasks.
HEG/EEG Nuerofeedback Brain-training program in Cheyenne, Wyoming [Pamphlet] ...Jacob Stotler
Brain-training uses HEG or EEG devices to monitor brain activity and aims to gain voluntary control over behaviors, habits, and patterns. It can help treat disorders like ADHD, anxiety, and depression. The training involves seeing feedback on one's brain activation, working to control targeted areas, and establishing long-term changes through awareness, movement, and practice. Notable effects can occur within two weeks when doing one to three sessions per week, lasting 15-45 minutes each.
This study examined patterns of cortical activity using four different EEG testing protocols: an extended 8-minute baseline with alternating eyes open and closed, viewing a 10-minute dyadic social interaction video, and completing a personality survey. The protocols elicited different levels of activity in the alpha band and theta/beta ratio. Specifically, all protocols differed significantly in alpha activity, and the three eyes-open conditions still differed when just compared to each other. This suggests protocol design impacts EEG results and more representative real-world designs should be explored.
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
The detection and diagnosis of various neurological disorders are performed using different medical
devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though
significant progress had been made in the analysis of EEG for diagnosis of different neurological
disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the
EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG
patterns could be used for early detection of CP. Twenty children participated in this study out of which ten
were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded
from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15
minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm
in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak
at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of
spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz.
The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can
be used for early detection of spastic CP.
Neurofeedback is a non-invasive treatment that uses EEG brainwave feedback to teach patients to self-regulate their brain function. It addresses issues like ADHD, autism, learning disorders, seizures, headaches and mood/behavioral issues. The technique measures a patient's brainwaves during computerized tasks and rewards productive patterns while the patient learns to control unproductive patterns. Studies show neurofeedback improves symptoms for these conditions and can help reduce medications. It works by helping the brain retrain itself into more optimal functioning.
Presented at a Bangladesh Child and Adolescent Mental Health (BACAMH) Conference at Bangabandhu Sheikh Medical University(BSMMU), Dahaka, Bangladesh to inform Health Professionals about Neurofeedbackand its therapeutic importance.
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES acijjournal
Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation. Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world -- for example, in
the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships. We will look at recent trends in functional magnetic resonance imaging (fMRI), with a special focus on the questions that have been addressed. This focus is
particularly important for functional neuroimaging, whose contributions will be measured by the depth of the questions asked. The ever-increasing understanding of the brain and behavior at work in the real world, the development of theoretical underpinnings, and the relentless spread of facilitative technology in the West and abroad are inexorably broadening the substrates for this interdisciplinary area of
research and practice. Neuroergonomics blends neuroscience and ergonomics to the mutual benefit of both fields, and extends the study of brain structure and function beyond the contrived laboratory settings often used in neuropsychological, psychophysical, cognitive science, and other neurosciencerelated fields. Neuroergonomics is providing rich observations of the brain and behavior at work, at home, in
transportation, and in other everyday environments in human operators who see, hear, feel, attend, remember, decide, plan, act, move, or manipulate objects among other people and technology in diverse, real-world settings. The neuroergonomics approach is allowing researchers to ask different questions
and develop new explanatory frameworks about humans at work in the real world and in relation to modern automated systems and machines, drawing from principles of neuropsychology, psychophysics, neurophysiology, and anatomy at neuronal and systems levels. The neuroergonomics approach allows researchers to ask different questions and develop new explanatory frameworks about humans at work in
the real world and in relation to modern automated systems and machines. Better understanding of brain function can, for example, provide important guidelines and constraints for theories of information presentation and task design, optimization of alerting and warning signals, development of neural prostheses, and the design of robots. As an interdisciplinary endeavor, neuroergonomics will continue to
benefit from and grow alongside developments in neuroscience, psychology,
This document discusses neuroplasticity and its implications for rehabilitation after neurological injury or disorders. It defines neuroplasticity as the brain's ability to reorganize and form new connections in response to experiences and environment. The key mechanisms of neuroplasticity occur at the synaptic and structural levels through processes like long-term potentiation and neurogenesis. Leveraging neuroplasticity in rehabilitation involves targeted interventions like task-specific training, sensory stimulation, and cognitive exercises to promote brain reorganization and recovery of functions. A multidisciplinary approach and the individual's active participation are important for success.
Similar to INSIGHTS IN EEG VERSUS HEG AND RT-FMRI NEURO FEEDBACK TRAINING FOR COGNITION ENHANCEMENT (20)
PhotoQR: A Novel ID Card with an Encoded Viewgerogepatton
There is an increasing interest in developing techniques to identify and assess data to allow an easy and
continuous access to resources, services or places that require thorough ID control. Usually, in order to
give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries
without the need of extra credentials, a new system –named photoQR, is here proposed. This system is
based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl
techniques) and one QR code containing embedded data related to the picture. The idea is that the picture
and the QR code can assess each other by a proper hash value in the QR. The QR without the picture
cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented
in Python and can be used both in offline and real-time environments, which effectively combines security
concepts and image processing algorithms to obtain data assessment.
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
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INSIGHTS IN EEG VERSUS HEG AND RT-FMRI NEURO FEEDBACK TRAINING FOR COGNITION ENHANCEMENT
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
DOI: 10.5121/ijaia.2016.7602 17
INSIGHTS IN EEG VERSUS HEG AND RT-FMRI
NEURO FEEDBACK TRAINING FOR COGNITION
ENHANCEMENT
Antonia Plerou and Panagiotis Vlamos
Department of Informatics, Ionian University, Greece
ABSTRACT
Innovative research technologies in the neurosciences have remarkably improved the perception of brain
structure and function. The use of several neurofeedback training techniques is broadly used for the
memory and cognition augmentation as well as for several learning difficulties and AHDD rehabilitation.
Author’s objective is to review cognitive enhancement techniques with the use of brain imaging
intervention methods as well to evaluate the effects of these methods in the educational process. The
efficiency and limitations of neurofeedback training with the use of EEG brain imaging, HEG scanning,
namely NIR and PIR method and fMRI scan including rt-fMRI brain scanning technique are also
discussed. Moreover, technical and clinical details of several neurofeedback treatment approaches were
also taken into consideration.
KEYWORDS
Cognition; EEG signal; fMRI; HEG; learning difficulties; memory; neurofeedback training; rehabilitation
1. INTRODUCTION
Neuroscience methods combined with cognitive sciences have gradually created a new principle
in educational studies, in order to evaluate among others the influence in cognition [1].
Neuroscience is a field related to fields of neurology, physiology, and biology while the
neuroscience conflict with education is related to the development of techniques for brain
imaging and enhancing human brain performance during performing several cognitive functions.
Research with the use of cognitive neuroimaging provides evidence for several brain function
better perception. These functions better perception could be used in order to enhance educational
methods related with complex processes underpinning speech and language, thinking and
reasoning, reading, and mathematics.
Research methods blending neuroscience and educational perspectives involve the use brain
imaging tools to evaluate cognitive functions and enhance educational results in practice.
Considering the fact that genetic factors and brain physiology affect brain function, cognitive
abilities and mathematical perception could be evaluated within this frame with the use of
imaging techniques. Neurofeedback is an approach for non-invasive modulation of human brain
activity in order to manage mental disorders and enhance cognitive performance. The most
common neurofeedback training techniques are implemented with EEG brain signal
implementation. Nevertheless, neurofeedback training session is implemented as well with the
use several brain imaging techniques besides EEG, like real-time functional magnetic resonance
imaging (rtfMRI) or Hemoencephalography (HEG). Namely for the most frequently used
neurofeedback technique 2 to 4 surface electrodes are used to change the amplitude or speed of
specific brain waves in particular brain locations to treat ADHD, anxiety, and insomnia [2]. The
Hemoencephalographic (HEG) neurofeedback provides feedback on cerebral blood flow to treat
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
18
migraine [3], while the most recent type of neurofeedback, the functional magnetic resonance
imaging (fMRI) is used to regulate brain activity based on the activity feedback from deep
subcortical areas of the brain [4]. The aim of these neurofeedback training approaches is to
modify brain activity in a different way which is based on particular neurological measures
according to the used method.
2. EDUCATIONAL NEUROSCIENCE
Educational neuroscience is blending scientific fields like neuroscience, cognitive sciences, and
education. Educational neuroscience methods focus on accessing real-time information about the
brain in order to enhance cognitive functions like language, speech, emotion, consciousness,
attention, memory, and other higher cognitive functions [5] is involved with the use of
neuroscience-based techniques in order to enhance cognitive function. Neuroenhancement focus
in the human brain and nervous system, altering its properties to increase performance for
specific cognitive functions [6].
Research within the frame of Neuroeducation aims to enhance the relation between education and
neuroscience, while the ability of the brain to learn is investigated within the context of
education. Research design and methods in educational neuroscience involve neuroscientific tools
such as brain image technologies to investigate cognitive functions and inform educational
practices. The study of various aspects of neuroscience which relate to the task at hand aims to
provide improved learning and new teaching methods. Educational neuroscience, also called
neuroeducation, refers to educators and neuroscience researcher’s collaboration in order to
improve both the education and neuroscientific research. It is a recent interdisciplinary scientific
field that brings together researchers of developmental cognitive neuroscience, educational
psychology, technology, and theory as well as other related disciplines. Researchers of
educational neuroscience review the neural mechanisms of reading, numerical cognition,
attention in combination to learning difficulties including dyslexia, dyscalculia, and ADHD.
3. NEUROFEEDBACK TRAINING
Neurofeedback is a training technique that helps individuals learn how to self-regulate brain
activity with the use of neurological feedback provided by sensory devices. During a
neurofeedback training, the participant has to control and modify the amplitude, frequency or
coherence of the electrical signal. Neurofeedback session includes several types of training like
EEG neurofeedback training, HEG neurofeedback, and real-time fMRI neurofeedback training.
These neurofeedback training approaches modify brain activity in a different way which is based
on particular neurological measures. A number of related research provides evidence that
neurofeedback training is efficiently used in order to improve neuropsychological disorders,
improve cognitive efficiency, and enhance brain functions in healthy individuals [7].
3.1 NEURO FEEDBACK TRAINING TYPES
In order enhance several cognitive functions or improve brain disorders several types
neurofeedback training methods depending on treatment case. For instance, slow corticalpotential
neurofeedback (SCP-NF) improves the direction of slow cortical potentials to treat ADHD,
epilepsy, and migraines [8]. Additionally, low energy neurofeedback system (LENS)supplies a
weak electromagnetic signal to alter the patient’s brain waves while they aremotionless with their
eyes closed. This neurofeedback method is useful for traumatic brain injury, ADHD, insomnia,
fibromyalgia, restless legs syndrome, anxiety, depression, and anger treatment [9]. Moreover, live
Z-score neurofeedback is used to treat insomnia. It introduces the continuous comparison of
variables of brain electrical activity to a systematic database to provide continuous feedback [10].
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
19
Moreover, low-resolution electromagnetic tomography (LORE-TA) includes the use of 19
electrodes to monitor phase, power, and coherence. This neurofeedback technique is used to deal
with addictions, depression, and obsessive-compulsive disorder [11]. Nevertheless, these
neurofeedback training techniques are beyond of this review objective, therefore, they are not
further analyzed.
3.2 EEG NEUROFEEDBACK TRAINING
Within neurofeedback process, participants receive feedback derived from a number of sensors or
a specified device which record brain activity. Neurofeedback is a form of behavioral intervention
in terms of improving the skills in the area of intelligence and brain activity. Neurofeedback
training targets on changing the brain activity in both healthy and mentally disabled people
[12].The most common type of neurofeedback training is EEG neurofeedback, where electrodes
are placed on a person’s scalp, detect and evaluate potential brain wave abnormalities, and control
the trainee in order to improve his brain activity consciously.
3.3 EEG NEUROFEEDBACK TRAINING PROTOCOLS
Neurofeedback treatment protocols mainly focus on the alpha, beta, delta, theta, and gamma
treatment or a combining the wave’s ratio namely, alpha-theta ratio, beta-theta ratio, etc.
However, protocols like alpha, beta, theta, and alpha-theta ratio are mainly used for cognition
enhancement. Alpha training is mostly used for stress and anxiety reducing, for memory and
mental performance enhancement, and for brain injuries treatment [13]. According to SMR-low
Beta protocol, EEG neurofeedback training focus on normalizing abnormal EEG frequencies,
decreasing Theta ratio and increasing sensory motor rhythm (SMR) activity (10–13 Hz) in order
to reach an improved cognitive performance. Beta training is used to improve concentration,
computational performance, cognitive processing, reading ability, obsessive-compulsive disorder
(OCD) symptoms, and insomnia. Additionally, neurofeedback training technique is used for
healthy individuals in order to control Alpha-Theta waves providing an essential enhancement to
the brain ability. Alpha-theta training is commonly used for reducing stress effect, while it
increases creativity, relaxation, and musical performance. Alpha training is conducted within
eyes-closed condition in order to increase theta ratio to alpha ratio with the use of auditory
feedback. Theta brain waves are related to a number of brain activities such as memory, emotion,
creativity, and meditation. Theta treatment is mainly used to reduce anxiety, depression, and
ADHD. Gamma waves are related to cognitive processing and memory. Gamma training is used
for cognition enhancement, mental sharpness improvement, information processing
augmentation, problem-solving tasks and short-term memory improvement [13].
EEG neurofeedback training is considered to be a safe and painless method, while sensors are
placed on the head and record the brain activity without interfering with this procedure. A typical
EEG Neurofeedback training session is about participants to be involved with a video game while
the brain waves derived from the sensors connected to their brain is recorded. During the game,
the mind waves are controlled by means of an amplifier and a computer so the needed feedback
to be provided to the trainee. Gradually the mind responds to the signs of the continuous feedback
in order to enhance and stabilize the participant state of mind in an improved state in terms of
attention and self-regulation. The brain activity waves, although they are unconscious processes
within this procedure, are set under individual’s control. EEG neurofeedback research mainly
focuses on the improvement of the symptoms cognitive disorders like Attention Deficit Activity
Disorder (ADHD) or several learning disabilities.
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3.4 HEG NEUROFEEDBACK
Hemoencephalography (HEG) is a neurofeedback method where the trainee regulates consciously
the cortical blood flow. This technique is based on the idea that neurological feedback provided
by the HEG device trainee could consciously control the unconscious blood flow process.
Normally it would be impossible to detect, HEG neurofeedback device provides information
about blood flow whether it is high or low in certain regions. HEG is considered to be an
effective method for alleviating unwanted psychological impairment stemming from poor blood
flow. Both the test of variables of attention (TOVA) which is designed to assess cognitive
function and Spect scan for measuring the cortical blood flow are used in order to perform HEG
neurofeedback training. HEG neurofeedback training method includes NIR (near infrared) and
PIR (passive infrared) techniques. Specifically, near-infrared HEG is designed to measure
changes in oxygen levels within the blood and passive infrared HEG incorporates with the
measurement of cortical heat and blood oxygen levels [8].
3.5 FMRI NEUROFEEDBACK TRAINING
Brain imaging fMRI scanners are designed to provide “real time” instantaneous feedback, thus
real-time fMRI neurofeedback is used to detect determine abnormal neural connectivity and
provide feedback to the trainee in order to chance it. Within the use of fMRI neurofeedback
technique, a non-invasively neural functioning in real time analysis is provided. The technique is
used for diagnosing diseases, monitoring the severity, and tracking the efficacy of therapeutic
interventions for mental illnesses such as ADHD, anxiety, and depression [4].
Specifically, the trainee could learn to control unconscious processes within the brain such as the
activation of certain neural correlates associated with a disorder. This method is based on the
brain’s ability to change itself or else self-directed neuroplasticity. Trainee lie under the rtfMRI
scanner in his brain activity could be evaluated in real time. Specifically, feedback regarding
regional activation is provided about the brain activity and whether is increasing or decreasing in
problematic areas. In the case that the activity is decreasing in problematic areas, the feedback is
a “green light” on a computer screen signaling the participant that he is doing the right thing. In
the case that activity is increasing in problematic areas, the feedback provided is a “red light” on
the computer program noticing the trainee that he is doing the wrong thing. With repeated
practice, the trainee would control the ability to consciously regulate his unconscious brain
activity process.
3.6 NEUROFEEDBACK AND BRAIN TRAINING LIMITATION
EEG Neurofeedback involves a brain–computer interface that leads users to learn how to control
their cortical oscillations. EEG Neurofeedback has broadly used brain disorders treatment,
nevertheless there no sufficient evidence in regards to the efficacy on brain functions. However,
while EEG has superior temporal resolution compared to standard fMRI, poor spatial resolution
limits the clinical utility of EEG [14]. Techniques like electroconvulsive therapy, vagus nerve
stimulation, deep brain stimulation, and transcranial magnetic stimulation or transcranial direct
current stimulation, are also proposed for the treatment of brain disorders while producing clinical
change via altered neuroplasticity. Each of these methods has potential benefits as well as
limitations related to spatial resolution or by their invasive nature [15].
4. NEUROFEEDBACK TRAINING AND COGNITION
Neurofeedback training is based on implicit and nonconscious learning and conditioning.The
executive system is a cognitive system in psychology that controls cognitive processes
alsoreferred as the executive function, supervisory attentional system, or cognitive control. These
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
21
functions are mainly located in prefrontal areas of the frontal lobe and they related with processes
like planning, working memory, attention, problem-solving, reasoning, inhibition, mental
flexibility, multi-tasking, initiation and monitoring of actions [16].
Zotev et al. combined two approaches of simultaneous multimodal rtfMRI and EEG
neurofeedback (rtfMRI–EEG-nf). RtfMRI and EEG-nf were applied for the training of emotional
self-regulation in healthy participants while dealing with a positive emotion induction task based
on retrieval of pleasant autobiographical memories. This case study reviled the feasibility of
simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG)
activities of the human brain. In addition, evidence related to enhanced cognitive therapeutic
approaches for major neuropsychiatric disorders, particularly depression were suggested [17].
In Ros et.al, study healthy participants, were examined during performing attentional tasks in
order to check the plastically of distinct fMRI networks after reduction of alpha rhythm versus a
sham-feedback condition. In comparison to sham-feedback, EEG neurofeedback technique
induced an increase of connectivity within regions of the salience network linked within intrinsic
alertness (dorsal anterior cingulate). These findings suggest a neurobehavioral relation with
brain's exquisite functional plasticity as well as for a temporally direct impact of EEG
neurofeedback technique on a basic cognitive control network [18].
Mckendrick et al. evaluated the impact of working memory training on brain function and
behavior. Participants were monitored with the use of near-infrared spectroscopy (NIRS) during
their involvement with a dual verbal-spatial working memory task. Subjects were either assigned
to an adaptive state whose working memory load was adjusted in accordance with performance,
and others to a yoked condition whose working memory load was determined based on the
performance of trainees in the adaptive state. Changes in cerebral hemodynamics in reference to
left DLPFC and right VLPFC were noticed to be associated with time participants spent in
training. The results of the comparison between adaptive and yoked training suggested
differences in the rostral prefrontal cortex. These findings were interpreted in terms of decreased
proactive interference, increased neural efficiency, reduced mental workload for stimulus
processing, and increased working memory capacity with training [19].
Strenziok et al. examined the effects of extensive video game training on performance, white
matter integrity, and brain functional connectivity in healthy older adults. Video game training
was used to associate the far transfer with altered attentional control demands mediated by the
dorsal attention network and trained sensory cortex. Cognitive performance, diffusion-derived
white matter integrity, and functional connectivity of the superior parietal cortex were evaluated
both before and after the neurofeedback training protocol implementation. Findings suggested
that both auditory perception and visuomotor-working memory training also lead to changes in
functional connectivity between superior parietal cortex and inferior temporal lobe [20].
Toomim et.al., suggests that that NIR HEG training can consciously enhance regional cerebral
oxygenation to specific areas of the brain and result in increased performance on cognitive tasks.
Specifically, after only ten 30-minute sessions of HEG brain exercise training, participants facing
several neurological disorders showed increases in attention and decreases in impulsivity to
within normal levels [8].
3.7 NEUROFEEDBACK TRAINING IMPACT IN ADHD
People with ADHD (attention-deficit/hyperactivity disorder) are noticed to have decreased blood
flow to the prefrontal cortex region. The use of HEG neurofeedback technique in order to
increase blood flow to this region is suggested. Learned enhancement of EEG frequency
components in the lower beta range is suggested to be effective with ADHD symptoms.
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
22
According to Egner et al., study neurofeedback protocols was applied to healthy volunteers in
order to evaluate the impact on behavioral and electrocortical attention measures. Operant
enhancement of a 12-15Hz component was linked to a reduction in commission errors and
improved perceptual sensitivity in the case of a continuous performance task (CPT). On the
contrary, the opposite relation was noticed in the case of 15-18Hz enhancement. Both 12-15Hz
and 15-18Hz enhancement were related to notable increases in P300 event-related brain potential
amplitudes in an auditory oddball task [21].
3.8 NEUROFEEDBACK TRAINING IMPACT IN MATHEMATICS COGNITION
Hashemian et al., an approach using neurofeedback enhance the significance of treatment in
learning disorders. In their study, third grade of primary school children was selected separated
into two equal groups while the first group which received neurofeedback treatment while the
other group received non-real neurofeedback treatment (sham or placebo). The two groups were
matched for age, education, sex and degree of intelligence and mathematics disorder
Neurofeedback treatment was performed based on enhancement of beta/theta ratio in CZ region.
The comparison between real and sham groups showed that the effect of real neurofeedback
therapy was significant comparing to the sham group. Statistical analysis showed that
neurofeedback improves mathematic performance in boys while the training efficiency was
noticed to be high even one year after treatment [22].
4. DISCUSSION
Although neurofeedback training has widely been used in order to enhance cognition,
neurofeedback training for mathematical thinking enhancement has been shortly utilized. Taken
into consideration the pros and cons of several neurofeedback techniques and after a literature
review and the generalized methodological outline, a specified research framework based on
theoretical and experimental research models about mathematical thinking is suggested. Author’s
future work leans towards an algorithmic thinking enhancement model in order to overall
promote adaptability intelligence and learning. Algorithmic problem solving requires some
abstract thinking and coming up with a creative solution. Considered the most complex of all
intellectual functions, algorithmic problem solving has been suggested as a higher-order cognitive
process that requires the modulation and control of more routine or fundamental skill. The
procedure of enhancing algorithmic problem-solving ability is central to the higher-order
cognitive skills (HOCS) [23]. Thereafter related work in reference with cognition enhancement
are studied, two neurofeedback protocols namely Alpha-Theta and SMR-low Beta protocol are
proposed for reasoning and algorithmic thinking enhancement. During the proposed method pilot
phase, both the Alpha-theta and SMR-Low Beta neurofeedback training protocol are implemented
in the case study participants. During neurofeedback training and algorithmic thinking evaluation
the electrical brain signal was recorded for both neurofeedback and non-neurofeedback group.
EEG brain scanning implemented during the pilot study suggest the qualitative difference
between neurofeedback and non-neurofeedback trained group electrical activity while dealing
with the same mental task. In Figure 1, the electrical signal in the case of a neurofeedback trained
participant is presented during the pilot implementation of SMR-Low Beta protocol. Additionally,
in Figure 2 the electrical activity in the case of a non-neurofeedback evaluated participant while
being in the same mental stage is presented.
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
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Figure 1: EEG Signal Recording During Neurofeedback Training
Figure 2: EEG Signal Recording During a Non-Neurofeedback Training
Additional research in the frame of the above directions are necessary in order to evaluate
quantitatively the difference between the neurofeedback and non-neurofeedback pilot trained
participants.
5. CONCLUSION
In this paper, clinical applications of neurofeedback training, various protocols of treatment and
the HEQ and fMRI neurofeedback methods in terms of cognition enhancement. Several related
case studies were analyzed in order to evaluate the effectiveness of each neurofeedback training
approach. Neurofeedback has widely been used on individuals with educational abnormalities and
it's another step for further discussion in cognitive science. Nevertheless, neuroimaging studies of
mathematical learning disabilities are still rare but dyscalculia is an area of increasing interest for
neuroscience researchers. Author’s future work leans towards neurofeedback training
implementation in the case of participants facing mathematical disorders in order to strengthen
algorithmic thinking abilities.
REFERENCES
[1] R. Anderson, B. Love, and M.-J. Tsai, “Neuroscience Perspectives for Science and Mathematics
Learning in Technology-Enhanced Learning Environments,” Int. J. Sci. …, vol. 12, no. 3, pp. 467–
474, Jun. 2014.
[2] Y. Ali, N.-A. Mahmood, and R. Samaneh, “Current Advances in Neurofeedback Techniques for the
Treatment of ADHD,” Biomed. Pharmacol. J., vol. 8, no. March Spl Edition, 2015.
[3] J. A. Carmen, Passive Infrared Hemoencephalography (pIR HEG). 2001.
[4] N. Weiskopf, “Real-time fMRI and its application to neurofeedback,” Neuroimage, vol. 62, no. 2, pp.
682–692, 2012.
[5] H. Lalancette and S. R. Campbell, “Educational neuroscience: Neuroethical considerations,” Int. J.
Environ. Sci. Educ., vol. 7, no. 1, pp. 37–52, 2012.
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
24
[6] V. P. Clark, “Neuroenhancement: Enhancing brain and mind in health and in disease,” NeuroImage,
vol. 85. pp. 889–894, 2014.
[7] S. Wyckoff, N. Birbaumer, S. Wyckoff, and N. Birbaumer, “Neurofeedback,” in The Wiley
Handbook of Cognitive Behavioral Therapy, Chichester, UK: John Wiley & Sons, Ltd, 2013, pp.
273–310.
[8] H. Toomim, W. Mize, P. C. Kwong, M. Toomim, R. Marsh, G. P. Kozlowski, M. Kimball, and A.
Rémond, “Intentional Increase of Cerebral Blood Oxygenation Using Hemoencephalography (HEG):
An Efficient Brain Exercise Therapy,” J. Neurother., vol. 8, no. 3, pp. 5–21, Jan. 2005.
[9] L. Ochs, “The Low Energy Neurofeedback System (LENS): Theory, Background, and Introduction,”
J. Neurother., 2008.
[10] E. Hurt, L. E. Arnold, and N. Lofthouse, “Quantitative EEG Neurofeedback for the Treatment of
Pediatric Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorders, Learning Disorders,
and Epilepsy,” Child Adolesc. Psychiatr. Clin. N. Am., vol. 23, no. 3, pp. 465–486, 2014.
[11] R. W. Thatcher and J. Lubar, “Quantitative EEG and Neurofeedback in Children and Adolescents:
Anxiety Disorders, Depressive Disorders, Comorbid Addiction and Attention-deficit/Hyperactivity
Disorder, and Brain Injury,” Child Adolesc. Psychiatr. Clin. N. Am., vol. 23, no. 3, pp. 427–464,
2014.
[12] E. Mosanezhad Jeddi and M. A. Nazari, “Effectiveness of EEG-Biofeedback on Attentiveness,
Working Memory and Quantitative Electroencephalography on Reading Disorder.,” Iran. J.
psychiatry Behav. Sci., vol. 7, no. 2, pp. 35–43, 2013.
[13] H. Marzbani, H. R. Marateb, and M. Mansourian, “Neurofeedback: A Comprehensive Review on
System Design, Methodology and Clinical Applications.,” Basic Clin. Neurosci., vol. 7, no. 2, pp.
143–58, Apr. 2016.
[14] R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V.
Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis.,” J.
Neuroeng. Rehabil., vol. 5, no. 1, p. 25, Jan. 2008.
[15] L. E. Stoeckel, K. A. Garrison, S. Ghosh, P. Wighton, C. A. Hanlon, J. M. Gilman, S. Greer, N. B.
Turk-Browne, M. T. DeBettencourt, D. Scheinost, C. Craddock, T. Thompson, V. Calderon, C. C.
Bauer, M. George, H. C. Breiter, S. Whitfield-Gabrieli, J. D. Gabrieli, S. M. LaConte, L. Hirshberg, J.
A. Brewer, M. Hampson, A. Van Der Kouwe, S. Mackey, and A. E. Evins, “Optimizing real time
fMRI neurofeedback for therapeutic discovery and development.,” NeuroImage. Clin., vol. 5, pp.
245–55, Jan. 2014.
[16] M. Naeeimi, S. Ali Hosseini, A. Biglarian, N. Amiri, and E. Pishyareh, “Effectiveness of Audiovisual
Stimulation on Executive function in Children with High-functioning Autism,” Iran. Rehabil. J., vol.
11, 2013.
[17] V. Zotev, R. Phillips, H. Yuan, M. Misaki, and J. Bodurka, “Self-regulation of human brain activity
using simultaneous real-time fMRI and EEG neurofeedback.,” Neuroimage, vol. 85 Pt 3, pp. 985–95,
Jan. 2014.
[18] T. Ros, J. Théberge, P. A. Frewen, R. Kluetsch, M. Densmore, V. D. Calhoun, and R. A. Lanius,
“Mind over chatter: plastic up-regulation of the fMRI salience network directly after EEG
neurofeedback.,” Neuroimage, vol. 65, pp. 324–35, Jan. 2013.
[19] R. McKendrick, H. Ayaz, R. Olmstead, and R. Parasuraman, “Enhancing dual-task performance with
verbal and spatial working memory training: continuous monitoring of cerebral hemodynamics with
NIRS,” Neuroimage, vol. 85, pp. 1016–1028, 2014.
[20] M. Strenziok, R. Parasuraman, E. Clarke, D. Cisler, J. Thompson, and P. M. Greenwood,
“Neurocognitive enhancement in older adults: comparison of three cognitive training tasks to test a
hypothesis of training transfer in brain connectivity,” Neuroimage, vol. 85, pp. 1029–1041, 2014.
[21] T. Egner and J. H. Gruzelier, “Learned self-regulation of EEG frequency components affects attention
and event-related brain potentials in humans.,” Neuroreport, vol. 12, no. 18, pp. 4155–9, Dec. 2001.
[22] P. Hashemian and P. Hashemian, “Effectiveness of Neuro-feedback on Mathematics Disorder,” J.
Psychiatry, vol. 18, no. 2, 2015.
[23] D. Ben-Chaim and U. Zoller, “Self-Perception versus Students’ Perception of Teachers’ Personal
Style in College Science and Mathematics Courses,” Res. Sci. Educ., vol. 31, no. 3, pp. 437–454,
2001.
9. International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 6, November 2016
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AUTHORS
Dr. Antonia Plerou is a member of the Bioinformatics and Human Electrophysiology Laboratory
(BiheLab) of the Ionian University with field of expertise “Pattern recognition analyst for Neuroeducational
studies”. She received her Ph.D. degree from the Department of Informatics of the Ionian University in
Corfu. She studied Applied Mathematics at the Faculty of Sciences in the Aristotle University of
Thessaloniki and obtained her Master Degree in Mathematics from the Faculty of Sciences and Technology
of the Greek Open University. She has (co-) authored more than 20 articles in international conferences and
journals and 2 book chapters. Her research interests are mainly related to the fields of Cognitive Science
and Learning Difficulties, Dyscalculia and Algorithmic Thinking Difficulties, Artificial Neural Networks
and Algorithms, Artificial Intelligence and Pattern Recognition, Neuronal Disorders treatment using
Neuroinformatics, Neurofeedback Training, Neurodegenerative Diseases, and Educational Neuroscience
Dr. Panayiotis Vlamos is a Professor and Head of the Department of Informatics at the Ionian University.
He received his Diploma in Mathematics from the University of Athens and his Ph.D. degree in
Mathematics from the National Technical University of Athens, Greece. He is the director of
“Bioinformatics and Human Electrophysiology Lab” and of “Computational Modeling Lab” at the
Department of Informatics, Ionian University. He has (co-) authored more than 130 papers in international
journals, conferences and book chapters. He is also (co-) author of more than 16 educational books and
creator of several educational materials. He has been the principal researcher or a member of several
research projects on Mathematical Modeling and Simulation.