This document summarizes a project on developing a brain-computer interface system to help people with disabilities control external devices. It lists the supervisors and team members working on the project. It then outlines the agenda which includes defining the problem, objectives, motivations, system architecture, implementation, and future work. It notes disability statistics in Egypt and the objective is to help people overcome disabilities. The motivations include the technology now being more successful and dealing with new techniques. The system architecture involves acquiring brain signals, preprocessing, feature extraction, classification, and decision steps. The implementation uses an EMOTIV headset and explores preprocessing, feature extraction using wavelets, Fourier transforms and PCA, and classification using neural networks. Future work involves adding more
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
Combining Optical Brain Imaging and Physiological Signals to Study Cognitive ...InsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and fNIR Devices, Dr. Hasan Ayaz, Dr. Kurtulus Izzetoglu and Frazer Findlay present new research capabilities enabled through the integration of optical brain imaging technology and physiological recording systems.
Key topics covered during this webinar include physiological and physical principles of optical brain imaging, theory of operation, hardware and software integration, essential fNIR signal processing (demonstrated using fnirSoft analysis software), common field applications of fNIR imaging, why and how researchers can measure physiological data such as EDA, HR and ECG and acquistion procedures for co-registration of fNIR data and physiological monitoring signals using AcqKnowledge data acquisition and analysis software.
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
This document will examine issues pertaining to feature extraction, classification and prediction. It will
consider the application of these techniques to unlabelled Electroencephalogram (E.E.G.) data in an
attempt to discriminate between left and right hand imagery movements
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
Combining Optical Brain Imaging and Physiological Signals to Study Cognitive ...InsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and fNIR Devices, Dr. Hasan Ayaz, Dr. Kurtulus Izzetoglu and Frazer Findlay present new research capabilities enabled through the integration of optical brain imaging technology and physiological recording systems.
Key topics covered during this webinar include physiological and physical principles of optical brain imaging, theory of operation, hardware and software integration, essential fNIR signal processing (demonstrated using fnirSoft analysis software), common field applications of fNIR imaging, why and how researchers can measure physiological data such as EDA, HR and ECG and acquistion procedures for co-registration of fNIR data and physiological monitoring signals using AcqKnowledge data acquisition and analysis software.
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
This document will examine issues pertaining to feature extraction, classification and prediction. It will
consider the application of these techniques to unlabelled Electroencephalogram (E.E.G.) data in an
attempt to discriminate between left and right hand imagery movements
Aim of a brain-computer interface (BCI) is to provide a communication channel for paralyzed patients to interact with the outer world. I will start with the motivation behind brain-computer interfaces followed by description of a general BCI. I will then describe various kinds of BCI experiments and methods.
From the Un-Distinguished Lecture Series (http://ws.cs.ubc.ca/~udls/). The talk was given Jun. 8, 2007.
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...Md Kafiul Islam
This research presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 - 30 Hz) into account to separate artifacts from seizures. It requires a reference seizure epoch of N-sec which can either be generated from a patient-specific
seizure database (if available) or can be simulated by a simple mathematical model of seizure. The purpose of the algorithm is to reduce as much artifacts as possible without distorting the desired seizure events to be detected/diagnosed. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data:
fully simulated, semi-simulated and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for detection of seizures from non-seizure epochs have been found to be easily distinguishable after artifacts are removed and consequently reduces the false alarms in seizure detection. Results from an extensive experiment with these datasets prove the efficacy of
the proposed algorithm and hence this algorithm (with some modifications) is expected to be a future candidate for artifact removal not only in epilepsy diagnosis applications but also in other applications (e.g. BCI or other neuroscience studies).
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
- To study the behavior and properties of bio-electric signals.
- Develop a system to identify and recognize patterns of signals on a portable computer.
With the introduction of Blue Brain technology, which is a reverse engineering, we can overcome all the brain disorders and diseases. Blue Brain is the name of the world’s first virtual brain which makes a machine, function as a human brain. Even after the death of the person the complete functional attribute of a human brain can be stored in that and can be used for further development.
SF Big Analytics20170706: What the brain tells us about the future of streami...Chester Chen
Much of the world’s data is becoming streaming, time-series data. It becomes increasingly important to analyze streaming data in real-time. Hierarchal Temporal Memory (HTM) is a detailed computational theory of the neocortex. At the core of HTM are time-based learning algorithms that store and recall spatial and temporal patterns. HTM is well suited to a wide variety of problems; particularly those involve streaming data and time-based patterns. The current HTM systems are able to learn the structure of streaming data, make predictions and detect anomalies. It is distinguished from other techniques in its ability to learn continuously in a fully unsupervised manner. HTM has been tested and implemented in software, all of which is developed with best practices and is suitable for deploying in commercial applications. The core learning algorithms are fully documented and available in an open source project called NuPIC. HTM not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.
Speaker
Yuwei Cui a Research Staff Member at Numenta, a company focused on Machine Intelligence. His professional interests are in the areas of Artificial Intelligence, Computational Neuroscience, Computer Vision and Machine Learning. He became interested in AI while studying physics in the University of Science and Technology of China
He later went on to get a PhD in computational neuroscience, specializing in understanding how our visual system process sensory inputs and contribute to perceptions, from the University of Maryland at College Park. He became fascinated by the brain and reverse engineering its underlying computational principles. He has published numerous peer-reviewed scientific articles in Neuroscience and AI.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry PiVictor Asanza
This work presents the experimental design for recording Electroencephalography (EEG) signals in 20 test subjects submitted to Steady-state visually evoked potential (SSVEP). The stimuli were performed with frequencies of 7, 9, 11 and 13 Hz. Furthermore, the implementation of a classification system based on SSVEP-EEG signals from the occipital region of the brain obtained with the Emotiv EPOC device is presented. These data were used to train algorithms based on artificial intelligence in a Raspberry Pi 4 Model B. Finally, this work demonstrates the possibility of classifying with times of up to 1.8 ms in embedded systems with low computational capacity.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Aim of a brain-computer interface (BCI) is to provide a communication channel for paralyzed patients to interact with the outer world. I will start with the motivation behind brain-computer interfaces followed by description of a general BCI. I will then describe various kinds of BCI experiments and methods.
From the Un-Distinguished Lecture Series (http://ws.cs.ubc.ca/~udls/). The talk was given Jun. 8, 2007.
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...Md Kafiul Islam
This research presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 - 30 Hz) into account to separate artifacts from seizures. It requires a reference seizure epoch of N-sec which can either be generated from a patient-specific
seizure database (if available) or can be simulated by a simple mathematical model of seizure. The purpose of the algorithm is to reduce as much artifacts as possible without distorting the desired seizure events to be detected/diagnosed. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data:
fully simulated, semi-simulated and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for detection of seizures from non-seizure epochs have been found to be easily distinguishable after artifacts are removed and consequently reduces the false alarms in seizure detection. Results from an extensive experiment with these datasets prove the efficacy of
the proposed algorithm and hence this algorithm (with some modifications) is expected to be a future candidate for artifact removal not only in epilepsy diagnosis applications but also in other applications (e.g. BCI or other neuroscience studies).
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
- To study the behavior and properties of bio-electric signals.
- Develop a system to identify and recognize patterns of signals on a portable computer.
With the introduction of Blue Brain technology, which is a reverse engineering, we can overcome all the brain disorders and diseases. Blue Brain is the name of the world’s first virtual brain which makes a machine, function as a human brain. Even after the death of the person the complete functional attribute of a human brain can be stored in that and can be used for further development.
SF Big Analytics20170706: What the brain tells us about the future of streami...Chester Chen
Much of the world’s data is becoming streaming, time-series data. It becomes increasingly important to analyze streaming data in real-time. Hierarchal Temporal Memory (HTM) is a detailed computational theory of the neocortex. At the core of HTM are time-based learning algorithms that store and recall spatial and temporal patterns. HTM is well suited to a wide variety of problems; particularly those involve streaming data and time-based patterns. The current HTM systems are able to learn the structure of streaming data, make predictions and detect anomalies. It is distinguished from other techniques in its ability to learn continuously in a fully unsupervised manner. HTM has been tested and implemented in software, all of which is developed with best practices and is suitable for deploying in commercial applications. The core learning algorithms are fully documented and available in an open source project called NuPIC. HTM not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.
Speaker
Yuwei Cui a Research Staff Member at Numenta, a company focused on Machine Intelligence. His professional interests are in the areas of Artificial Intelligence, Computational Neuroscience, Computer Vision and Machine Learning. He became interested in AI while studying physics in the University of Science and Technology of China
He later went on to get a PhD in computational neuroscience, specializing in understanding how our visual system process sensory inputs and contribute to perceptions, from the University of Maryland at College Park. He became fascinated by the brain and reverse engineering its underlying computational principles. He has published numerous peer-reviewed scientific articles in Neuroscience and AI.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry PiVictor Asanza
This work presents the experimental design for recording Electroencephalography (EEG) signals in 20 test subjects submitted to Steady-state visually evoked potential (SSVEP). The stimuli were performed with frequencies of 7, 9, 11 and 13 Hz. Furthermore, the implementation of a classification system based on SSVEP-EEG signals from the occipital region of the brain obtained with the Emotiv EPOC device is presented. These data were used to train algorithms based on artificial intelligence in a Raspberry Pi 4 Model B. Finally, this work demonstrates the possibility of classifying with times of up to 1.8 ms in embedded systems with low computational capacity.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Development of wearable object detection system & blind stick for visuall...Arkadev Kundu
It is a wearable device. It has a camera, and it detects all living and non living object. This module detects moving object also. It is made with raspberry pi 3, and a camera. One headphone connect with raspberry pi. When this module detects items, it gave a sound output through headphone. Hence the blind man know that item, which is in-front of him or her. We made it in very low budget, and it is very helpful for visually challenged people. And the Blind stick help him to detect obstacles.
This presentation shows the impact of GPU computing on cognitive robotics by showing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amount of computational power, which was until recently provided mostly by standard CPU processors. However, CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. However, this impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This presentation shows several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity, which enabled conducting the novel experiments described herein.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stockrebeccabio
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Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
7. Project Motivation
• A lot of people cannot imagine how this system
will be done and used.
• This project not really popular in Egypt “till
now”.
• Recently, intense research has been conducted in
BCI technology
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8. Project Motivation cont.
• And now many projects reach the levels of
success originally touted.
• We will deal with new technology and
implement it by using new techniques.
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9. System architecture
Signal
Preprocessing Feature Extraction
Acquisition
Decision Classification
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10. System Acquisition
How to explore brain activity?
Invasive Noninvasive
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11. EMOTIV Headset
• The EMOTIV Headset (EPOC) has 14 electrodes
(compared to the 19 electrodes of a standard
medical EEG).
• We use only 5 channels (AF3-F7-F3-FC5-P7)
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12. Preprocessing
• there are two purpose for preprocessing
KeepRemove artifacts signals: certain frequency
interest in EEG signals in
band(0.5-45)
Band Pass Filter
Biological Environmental
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14. Fourier
provides a signal which is localized
only in the Frequency domain.
Features are magnitude values for the
specified spectral range of frequencies
Ex: 1-Range(8-30) = 23 features for each channel
2-Top Ten Frequencies for each channel 14
15. Wavelet packet decomposition WPD:
• Is localized in both time and frequency
•Divided signal into component according to time
•Parameters : according to the required Band and the
sampling rate we select the number of levels for our
WPD
•Features : Mu-Sigma-Min-Max-Epsilon (30 features for
each channel)
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16. Principal Components
Analysis
• It is a way of identifying patterns in data,
and expressing the data in such a way as
to highlight their similarities and
differences
• The other main advantage of PCA is that
once you have found these patterns in
the data, and you compress the data
without much loss of information.
• (5 features for each channel)
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17. Classification
In this step we need to classify the signal to detect
the Arm motion
Neural Networks
• A type of artificial intelligence that
attempts to imitate the way a human
brain works. Rather than using a digital
model.
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21. Future Work
• Add more movements of different parts of the
body
• Get the data from emotive headset to the arm
directly using wireless connection
• Implement the program on a microcontroller
in the arm
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22. References
[1] R. Palaniappan and D. P. Mandic. EEG based biometric framework for automatic identity
verification. Journal of VLSI Signal Processing
Systems, 49(2):243–250, 2007.
[2] R. Palaniappan and K. Ravi. Improving visual evoked potential feature classification for
person recognition using PCA and normalization.
Pattern Recognition Letters, 27(7):726 – 733, 2006.
[3] R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles’. The electroencephalogram as a
biometric. In Canadian Conference on Electrical and Computer Engineering, volume 2,
pages 1363 –1366, 2001.
[4] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Parametric person
identification from the EEG using computational
geometry. volume 2, pages 1005 –1008, Pafos, Cyprus, 1999.
[5] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Person identification based
on parametric processing of the EEG. volume 1, pages 283 –286, Pafos, Cyprus, 1999.
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The brain activity can be monitored via several methods, which can be classified as invasive and noninvasive. The invasive method need to per manently implant devices in the brain which generated many risks and it is not feasible in particle applications. The noninvasive methods include magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), optical imaging and elec troencephalography (EEG).