- The document analyzes brain cognitive states during an arithmetic task and motor task using electroencephalography (EEG) signals.
- EEG data was collected from 10 healthy volunteers during resting states, a motor task, and performing arithmetic calculations.
- The EEG signals were analyzed using standardized low resolution brain electromagnetic tomography (sLORETA) to generate 3D cortical distributions and localize the neuronal generators responsible for different cognitive states.
- The results were consistent with previous neuroimaging research, showing that EEG can demonstrate neuronal activity at the cortical level with good spatial resolution and provide both spatial and temporal information about cognitive functions.
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
Ā
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
Review:Wavelet transform based electroencephalogram methodsijtsrd
Ā
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Ā
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.
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
Ā
This project discussed about a brain controlled biometric based on Brainācomputer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
Ā
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of āYesā in the captured EEG, as opposed to a
certain pattern of āNoā when the user is relaxed. Accordingly, the task is to determine that the captured EEG is āYesā or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
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
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
Ā
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
Review:Wavelet transform based electroencephalogram methodsijtsrd
Ā
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Ā
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.
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
Ā
This project discussed about a brain controlled biometric based on Brainācomputer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
Ā
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of āYesā in the captured EEG, as opposed to a
certain pattern of āNoā when the user is relaxed. Accordingly, the task is to determine that the captured EEG is āYesā or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
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
Letās master the digital toolkit to harness lifelong neuroplasticitySharpBrains
Ā
Four leading pioneers of applied neuroplasticity helped us navigate best practices to harness most promising non-invasive neurotechnologies, such as cognitive training, mindfulness apps, EEG and virtual/ augmented reality.
--Chair: Linda Raines, CEO of the Mental Health Association of Maryland
--Dr. Michael Merzenich, winner of the 2016 Kavli Prize in Neuroscience
--Dr. Judson Brewer, Founder & Research Lead of Claritas Mindsciences
--Tan Le, CEO of Emotiv
--Dr. Andrea Serino, Head of Neuroscience at MindMaze
Learn more at sharpbrains.com
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
Ā
mHealth applications have shown promise in supporting the delivery of health services in peoplesā daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called āSehha & Mawidā Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developersā
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
Ā
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
Ā
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Ā
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
International Journal of Computational Engineering Research(IJCER)ijceronline
Ā
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Ā
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.
15 Trends In Neurotechnologies That Will Change The WorldNikita Lukianets
Ā
Below are technologies related to neuro and cognitive under three key areas of accelerating change: Machine Learning & Neural Network Computing, Extended Cognition and Neural Interfaces. Neural network computing will lead to improvements in computer vision and analysis, such as detecting emotions and moods, which may have safety and security applications. Extended cognition involves more direct connection to people's brains, allowing mood, thought patterns and information to be altered in the brain. Neural interfaces get information out of people's brains more efficiently, ultimately allowing a machine-enabled form of telepathy. This presentation covers Michell Zappa research from Policy Horizons Canada
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
Ā
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
Ā
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.
Amyotrophic Lateral Sclerosis (ALS) is the most common progressive neurodegenerative disorder reflecting
the degeneration of upper and lower motor neurons. Motor neurons controls the communication between nervous
system and muscles of the body. ALS results in the loss of voluntary control over muscular activities along with the
inability to breathe and the maximum life expectancy of affected individual will be 3-5 years from the onset of
symptoms. But the lifetime of affected people can be extended by early detection of disease. The usual methods for
diagnosis are Electromyography (EMG), Nerve Conduction Study (NCS), Magnetic Resonance Imaging (MRI) and
Magneto-encephalography (MEG). But some of these methods may erroneously result in neuropathy or myopathy
instead of ALS and some do not provide any biomarker. EEG is comparatively least expensive method and it
provides biomarker for ALS detection. ALS is always associated with fronto-temporal dementia (FTD). The spectral
analysis of EEG will reveal the structural and functional connectivity alterations of the underlying neural network
that occurs due to FTD and it can generate potential biomarkers for the early detection of ALS. A novel algorithm
has been developed by exploiting the Dual Tree Complex Wavelet Transform (DTCWT) technique and it can
overcome the short comes of existing methods for the analysis and feature extraction of EEG. Deterministic
biomarkers were obtained from spectral analysis of EEG and the proposed algorithm provided 100% accuracy for all
the test datasets.
Intelligence of a human being in general is considered as to its variations in the ability to learn, to function in society, and to behave according to contemporary social expectations Intelligence of a human being is associated with brain the brain is considered as the most complex biological existent structure.
Electroencephalograph (EEG) is an instrument used for recording the electrical activity of the brain. EEG is the variation of the electrical fields in the cortex or on the surface of scalp caused by the physiological activities of the brain. EEG is currently the most widely adopted method for assessing brain activities. Detecting the changes of these waves is critical for understanding of brain function. In clinical applications, spontaneous EEG signals can be divided into several rhythms according to their frequency. They are Ī“ rhythm (0.1-4Hz), Īø rhythm (4-8Hz), Ī± rhythm (8-13), Ī² and rhythm (13-30Hz). The EEG signals have close relationships with the cerebral diseases, mental status and human qualities like intelligence. As a consequence it is very useful to analyze process and classify the EEG signal on the basis of frequency bands and then extract their underlying features and so as to correlate with normal and abnormal functioning of brain, sleep, mental status and also with intelligence. In this paper we propose to conduct pointed literature survey of alpha activity and intelligence correlation. We propose to conduct test on subjects by computer, EEG interface. We propose to conclude from practical experimentation, whether there is a correlation between alpha activity power and intelligence.
Feature selection approach in animal classificationsipij
Ā
In this paper, we propose a model for automatic classification of Animals using different classifiers Nearest
Neighbour, Probabilistic Neural Network and Symbolic. Animal images are segmented using maximal
region merging segmentation. The Gabor features are extracted from segmented animal images.
Discriminative texture features are then selected using the different feature selection algorithm like
Sequential Forward Selection, Sequential Floating Forward Selection, Sequential Backward Selection and
Sequential Floating Backward Selection. To corroborate the efficacy of the proposed method, an
experiment was conducted on our own data set of 25 classes of animals, containing 2500 samples. The
data set has different animal species with similar appearance (small inter-class variations) across different
classes and varying appearance (large intra-class variations) within a class. In addition, the images of
flowers are of different poses, with cluttered background under different lighting and climatic conditions.
Experiment results reveal that Symbolic classifier outperforms Nearest Neighbour and Probabilistic Neural
Network classifiers.
Application of parallel algorithm approach for performance optimization of oi...sipij
Ā
This paper gives a detailed study on the performance of image filter algorithm with various parameters
applied on an image of RGB model. There are various popular image filters, which consumes large amount
of computing resources for processing. Oil paint image filter is one of the very interesting filters, which is
very performance hungry. Current research tries to find improvement in oil paint image filter algorithm by
using parallel pattern library. With increasing kernel-size, the processing time of oil paint image filter
algorithm increases exponentially. I have also observed in various blogs and forums, the questions for
faster oil paint have been asked repeatedly.
Offline handwritten signature identification using adaptive window positionin...sipij
Ā
The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning
technique which focuses on not just the meaning of the handwritten signature but also on the individuality
of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn
(13x13). This size should be large enough to contain ample information about the style of the author and
small enough to ensure a good identification performance. The process was tested with a GPDS dataset
containing 4870 signature samples from 90 different writers by comparing the robust features of the test
signature with that of the userās signature using an appropriate classifier. Experimental results reveal that
adaptive window positioning technique proved to be the efficient and reliable method for accurate
signature feature extraction for the identification of offline handwritten signatures .The contribution of this
technique can be used to detect signatures signed under emotional duress
Contrast enhancement using various statistical operations and neighborhood pr...sipij
Ā
Histogram Equalization is a simple and effective contrast enhancement technique. In spite of its popularity
Histogram Equalization still have some limitations āproduces artifacts, unnatural images and the local
details are not considered, therefore due to these limitations many other Equalization techniques have been
derived from it with some up gradation. In this proposed method statistics play an important role in image
processing, where statistical operations is applied to the image to get the desired result such as
manipulation of brightness and contrast. Thus, a novel algorithm using statistical operations and
neighborhood processing has been proposed in this paper where the algorithm has proven to be effective in
contrast enhancement based on the theory and experiment.
Review of ocr techniques used in automatic mail sorting of postal envelopessipij
Ā
This paper presents a review of various OCR techniq
ues used in the automatic mail sorting process. A
complete description on various existing methods fo
r address block extraction and digit recognition th
at
were used in the literature is discussed. The objec
tive of this study is to provide a complete overvie
w about
the methods and techniques used by many researchers
for automating the mail sorting process in postal
service in various countries. The significance of Z
ip code or Pincode recognition is discussed.
Letās master the digital toolkit to harness lifelong neuroplasticitySharpBrains
Ā
Four leading pioneers of applied neuroplasticity helped us navigate best practices to harness most promising non-invasive neurotechnologies, such as cognitive training, mindfulness apps, EEG and virtual/ augmented reality.
--Chair: Linda Raines, CEO of the Mental Health Association of Maryland
--Dr. Michael Merzenich, winner of the 2016 Kavli Prize in Neuroscience
--Dr. Judson Brewer, Founder & Research Lead of Claritas Mindsciences
--Tan Le, CEO of Emotiv
--Dr. Andrea Serino, Head of Neuroscience at MindMaze
Learn more at sharpbrains.com
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
Ā
mHealth applications have shown promise in supporting the delivery of health services in peoplesā daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called āSehha & Mawidā Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developersā
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
Ā
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
Ā
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Ā
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
International Journal of Computational Engineering Research(IJCER)ijceronline
Ā
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Ā
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.
15 Trends In Neurotechnologies That Will Change The WorldNikita Lukianets
Ā
Below are technologies related to neuro and cognitive under three key areas of accelerating change: Machine Learning & Neural Network Computing, Extended Cognition and Neural Interfaces. Neural network computing will lead to improvements in computer vision and analysis, such as detecting emotions and moods, which may have safety and security applications. Extended cognition involves more direct connection to people's brains, allowing mood, thought patterns and information to be altered in the brain. Neural interfaces get information out of people's brains more efficiently, ultimately allowing a machine-enabled form of telepathy. This presentation covers Michell Zappa research from Policy Horizons Canada
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
Ā
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
Ā
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.
Amyotrophic Lateral Sclerosis (ALS) is the most common progressive neurodegenerative disorder reflecting
the degeneration of upper and lower motor neurons. Motor neurons controls the communication between nervous
system and muscles of the body. ALS results in the loss of voluntary control over muscular activities along with the
inability to breathe and the maximum life expectancy of affected individual will be 3-5 years from the onset of
symptoms. But the lifetime of affected people can be extended by early detection of disease. The usual methods for
diagnosis are Electromyography (EMG), Nerve Conduction Study (NCS), Magnetic Resonance Imaging (MRI) and
Magneto-encephalography (MEG). But some of these methods may erroneously result in neuropathy or myopathy
instead of ALS and some do not provide any biomarker. EEG is comparatively least expensive method and it
provides biomarker for ALS detection. ALS is always associated with fronto-temporal dementia (FTD). The spectral
analysis of EEG will reveal the structural and functional connectivity alterations of the underlying neural network
that occurs due to FTD and it can generate potential biomarkers for the early detection of ALS. A novel algorithm
has been developed by exploiting the Dual Tree Complex Wavelet Transform (DTCWT) technique and it can
overcome the short comes of existing methods for the analysis and feature extraction of EEG. Deterministic
biomarkers were obtained from spectral analysis of EEG and the proposed algorithm provided 100% accuracy for all
the test datasets.
Intelligence of a human being in general is considered as to its variations in the ability to learn, to function in society, and to behave according to contemporary social expectations Intelligence of a human being is associated with brain the brain is considered as the most complex biological existent structure.
Electroencephalograph (EEG) is an instrument used for recording the electrical activity of the brain. EEG is the variation of the electrical fields in the cortex or on the surface of scalp caused by the physiological activities of the brain. EEG is currently the most widely adopted method for assessing brain activities. Detecting the changes of these waves is critical for understanding of brain function. In clinical applications, spontaneous EEG signals can be divided into several rhythms according to their frequency. They are Ī“ rhythm (0.1-4Hz), Īø rhythm (4-8Hz), Ī± rhythm (8-13), Ī² and rhythm (13-30Hz). The EEG signals have close relationships with the cerebral diseases, mental status and human qualities like intelligence. As a consequence it is very useful to analyze process and classify the EEG signal on the basis of frequency bands and then extract their underlying features and so as to correlate with normal and abnormal functioning of brain, sleep, mental status and also with intelligence. In this paper we propose to conduct pointed literature survey of alpha activity and intelligence correlation. We propose to conduct test on subjects by computer, EEG interface. We propose to conclude from practical experimentation, whether there is a correlation between alpha activity power and intelligence.
Feature selection approach in animal classificationsipij
Ā
In this paper, we propose a model for automatic classification of Animals using different classifiers Nearest
Neighbour, Probabilistic Neural Network and Symbolic. Animal images are segmented using maximal
region merging segmentation. The Gabor features are extracted from segmented animal images.
Discriminative texture features are then selected using the different feature selection algorithm like
Sequential Forward Selection, Sequential Floating Forward Selection, Sequential Backward Selection and
Sequential Floating Backward Selection. To corroborate the efficacy of the proposed method, an
experiment was conducted on our own data set of 25 classes of animals, containing 2500 samples. The
data set has different animal species with similar appearance (small inter-class variations) across different
classes and varying appearance (large intra-class variations) within a class. In addition, the images of
flowers are of different poses, with cluttered background under different lighting and climatic conditions.
Experiment results reveal that Symbolic classifier outperforms Nearest Neighbour and Probabilistic Neural
Network classifiers.
Application of parallel algorithm approach for performance optimization of oi...sipij
Ā
This paper gives a detailed study on the performance of image filter algorithm with various parameters
applied on an image of RGB model. There are various popular image filters, which consumes large amount
of computing resources for processing. Oil paint image filter is one of the very interesting filters, which is
very performance hungry. Current research tries to find improvement in oil paint image filter algorithm by
using parallel pattern library. With increasing kernel-size, the processing time of oil paint image filter
algorithm increases exponentially. I have also observed in various blogs and forums, the questions for
faster oil paint have been asked repeatedly.
Offline handwritten signature identification using adaptive window positionin...sipij
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The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning
technique which focuses on not just the meaning of the handwritten signature but also on the individuality
of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn
(13x13). This size should be large enough to contain ample information about the style of the author and
small enough to ensure a good identification performance. The process was tested with a GPDS dataset
containing 4870 signature samples from 90 different writers by comparing the robust features of the test
signature with that of the userās signature using an appropriate classifier. Experimental results reveal that
adaptive window positioning technique proved to be the efficient and reliable method for accurate
signature feature extraction for the identification of offline handwritten signatures .The contribution of this
technique can be used to detect signatures signed under emotional duress
Contrast enhancement using various statistical operations and neighborhood pr...sipij
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Histogram Equalization is a simple and effective contrast enhancement technique. In spite of its popularity
Histogram Equalization still have some limitations āproduces artifacts, unnatural images and the local
details are not considered, therefore due to these limitations many other Equalization techniques have been
derived from it with some up gradation. In this proposed method statistics play an important role in image
processing, where statistical operations is applied to the image to get the desired result such as
manipulation of brightness and contrast. Thus, a novel algorithm using statistical operations and
neighborhood processing has been proposed in this paper where the algorithm has proven to be effective in
contrast enhancement based on the theory and experiment.
Review of ocr techniques used in automatic mail sorting of postal envelopessipij
Ā
This paper presents a review of various OCR techniq
ues used in the automatic mail sorting process. A
complete description on various existing methods fo
r address block extraction and digit recognition th
at
were used in the literature is discussed. The objec
tive of this study is to provide a complete overvie
w about
the methods and techniques used by many researchers
for automating the mail sorting process in postal
service in various countries. The significance of Z
ip code or Pincode recognition is discussed.
A voting based approach to detect recursive order number of photocopy documen...sipij
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Photocopy documents are very common in our normal life. People are permitted to carry and present
photocopied documents to avoid damages to the original documents. But this provision is misused for
temporary benefits by fabricating fake photocopied documents. Fabrication of fake photocopied document
is possible only in 2nd and higher order recursive order of photocopies. Whenever a photocopied document
is submitted, it may be required to check its originality. When the document is 1st order photocopy, chances
of fabrication may be ignored. On the other hand when the photocopy order is 2nd or above, probability of
fabrication may be suspected. Hence when a photocopy document is presented, the recursive order number
of photocopy is to be estimated to ascertain the originality. This requirement demands to investigate
methods to estimate order number of photocopy. In this work, a voting based approach is used to detect the
recursive order number of the photocopy document using probability distributions exponential, extreme
values and lognormal distributions is proposed. A detailed experimentation is performed on a generated
data set and the method exhibits efficiency close to 89%.
Speaker Identification From Youtube Obtained Datasipij
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An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like
YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker
identification is to identify the number of different speakers and prepare a model for that speaker by
extraction, characterization and speaker-specific information contained in the speech signal. It has many
diverse application specially in the field of Surveillance , Immigrations at Airport , cyber security ,
transcription in multi-source of similar sound source, where it is difficult to assign transcription arbitrary.
The most commonly speech parameterization used in speaker verification, K-mean, cepstral analysis, is
detailed. Gaussian mixture modeling, which is the speaker modeling technique is then explained. Gaussian
mixture models (GMM), perhaps the most robust machine learning algorithm has been introduced to
examine and judge carefully speaker identification in text independent. The application or employment of
Gaussian mixture models for monitoring & Analysing speaker identity is encouraged by the familiarity,
awareness, or understanding gained through experience that Gaussian spectrum depict the characteristics
of speaker's spectral conformational pattern and remarkable ability of GMM to construct capricious
densities after that we illustrate 'Expectation maximization' an iterative algorithm which takes some
arbitrary value in initial estimation and carry on the iterative process until the convergence of value is
observed We have tried to obtained 85 ~ 95% of accuracy using speaker modeling of vector quantization
and Gaussian Mixture model ,so by doing various number of experiments we are able to obtain 79 ~ 82%
of identification rate using Vector quantization and 85 ~ 92.6% of identification rate using GMM modeling
by Expectation maximization parameter estimation depending on variation of parameter.
A Novel Uncertainty Parameter SR ( Signal to Residual Spectrum Ratio ) Evalua...sipij
Ā
Usually, hearing impaired people use hearing aids which are implemented with speech enhancement
algorithms. Estimation of speech and estimation of nose are the components in single channel speech
enhancement system. The main objective of any speech enhancement algorithm is estimation of noise power
spectrum for non stationary environment. VAD (Voice Activity Detector) is used to identify speech pauses
and during these pauses only estimation of noise. MMSE (Minimum Mean Square Error) speech
enhancement algorithm did not enhance the intelligibility, quality and listener fatigues are the perceptual
aspects of speech. Novel evaluation approach SR (Signal to Residual spectrum ratio) based on uncertainty
parameter introduced for the benefits of hearing impaired people in non stationary environments to control
distortions. By estimation and updating of noise based on division of original pure signal into three parts
such as pure speech, quasi speech and non speech frames based on multiple threshold conditions. Different
values of SR and LLR demonstrate the amount of attenuation and amplification distortions. The proposed
method will compared with any one method WAT(Weighted Average Technique) Hence by using
parameters SR (signal to residual spectrum ratio) and LLR (log like hood ratio), MMSE (Minim Mean
Square Error) in terms of segmented SNR and LLR.
Robust content based watermarking algorithm using singular value decompositio...sipij
Ā
Nowadays, image content is frequently subject to different malicious manipulations. To protect images
from this illegal manipulations computer science community have recourse to watermarking techniques. To
protect digital multimedia content we need just to embed an invisible watermark into images which
facilitate the detection of different manipulations, duplication, illegitimate distributions of these images. In
this work a robust watermarking technique is presented that embedding invisible watermarks into colour
images the singular value decomposition bloc by bloc of a robust transform of images that is the Radial
symmetry transform. Each bit of the watermark is inserted in a bloc of eight pixels large of the blue
channel a high singular value of the corresponding bloc into the radial symmetry map. We justified the
insertion in the blue channel by our feeble sensibility to perturbations in this colour channel of images. We
present also results obtained with different tests. We had tested the imperceptibility of the mark using this
approach and also its robustness face to several attacks.
IDENTIFICATION OF SUITED QUALITY METRICS FOR NATURAL AND MEDICAL IMAGESsipij
Ā
To assess quality of the denoised image is one of the important task in image denoising application.
Numerous quality metrics are proposed by researchers with their particular characteristics till today. In
practice, image acquisition system is different for natural and medical images. Hence noise introduced in
these images is also different in nature. Considering this fact, authors in this paper tried to identify the
suited quality metrics for Gaussian, speckle and Poisson corrupted natural, ultrasound and X-ray images
respectively. In this paper, sixteen different quality metrics from full reference category are evaluated with
respect to noise variance and suited quality metric for particular type of noise is identified. Strong need to
develop noise dependent quality metric is also identified in this work.
An intensity based medical image registration using genetic algorithmsipij
Ā
Medical imaging plays a vital role to create images of human body for clinical purposes. Biomedical
imaging has taken a leap by entering into the field of image registration. Image registration integrates the
large amount of medical information embedded in the images taken at different time intervals and images
at different orientations. In this paper, an intensity-based real-coded genetic algorithm is used for
registering two MRI images. To demonstrate the efficiency of the algorithm developed, the alignment of the
image is altered and algorithm is tested for better performance. Also the work involves the comparison of
two similarity metrics, and based on the outcome the best metric suited for genetic algorithm is studied.
Parallax Effect Free Mosaicing of Underwater Video Sequence Based on Texture ...sipij
Ā
In this paper, we present feature-based technique for construction of mosaic image from underwater video
sequence, which suffers from parallax distortion due to propagation properties of light in the underwater
environment. The most of the available mosaic tools and underwater image mosaicing techniques yields
final result with some artifacts such as blurring, ghosting and seam due to presence of parallax in the input
images. The removal of parallax from input images may not reduce its effects instead it must be corrected
in successive steps of mosaicing. Thus, our approach minimizes the parallax effects by adopting an efficient
local alignment technique after global registration. We extract texture features using Centre Symmetric
Local Binary Pattern (CS-LBP) descriptor in order to find feature correspondences, which are used further
for estimation of homography through RANSAC. In order to increase the accuracy of global registration,
we perform preprocessing such as colour alignment between two selected frames based on colour
distribution adjustment. Because of existence of 100% overlap in consecutive frames of underwater video,
we select frames with minimum overlap based on mutual offset in order to reduce the computation cost
during mosaicing. Our approach minimizes the parallax effects considerably in final mosaic constructed
using our own underwater video sequences.
Beamforming with per antenna power constraint and transmit antenna selection ...sipij
Ā
In this paper, transmit beamforming and antenna selection techniques are presented for the Cooperative
Distributed Antenna System. Beamforming technique with minimum total weighted transmit power
satisfying threshold SINR and Per-Antenna Power constraints is formulated as a convex optimization
problem for the efficient performance of Distributed Antenna System (DAS). Antenna Selection technique is
implemented in this paper to select the optimum Remote Antenna Units from all the available ones. This
achieves the best compromise between capacity and system complexity. Dual polarized and Triple
Polarized systems are considered. Simulation results prove that by integrating Beamforming with DAS
enhances its performance. Also by using convex optimization in Antenna Selection enhances the
performance of multi polarized systems.
Global threshold and region based active contour model for accurate image seg...sipij
Ā
In this contribution, we develop a novel global threshold-based active contour model. This model deploys a new
edge-stopping function to control the direction of the evolution and to stop the evolving contour at weak or
blurred edges. An implementation of the model requires the use of selective binary and Gaussian filtering
regularized level set (SBGFRLS) method. The method uses either a selective local or global segmentation
property. It penalizes the level set function to force it to become a binary function. This procedure is followed by
using a regularisation Gaussian. The Gaussian filters smooth the level set function and stabilises the evolution
process. One of the merits of our proposed model stems from the ability to initialise the contour anywhere inside
the image to extract object boundaries. The proposed method is found to perform well, notably when the
intensities inside and outside the object are homogenous. Our method is applied with satisfactory results on
various types of images, including synthetic, medical and Arabic-characters images.
Lossless image compression using new biorthogonal waveletssipij
Ā
Even though a large number of wavelets exist, one needs new wavelets for their specific applications. One
of the basic wavelet categories is orthogonal wavelets. But it was hard to find orthogonal and symmetric
wavelets. Symmetricity is required for perfect reconstruction. Hence, a need for orthogonal and symmetric
arises. The solution was in the form of biorthogonal wavelets which preserves perfect reconstruction
condition. Though a number of biorthogonal wavelets are proposed in the literature, in this paper four new
biorthogonal wavelets are proposed which gives better compression performance. The new wavelets are
compared with traditional wavelets by using the design metrics Peak Signal to Noise Ratio (PSNR) and
Compression Ratio (CR). Set Partitioning in Hierarchical Trees (SPIHT) coding algorithm was utilized to
incorporate compression of images.
A combined method of fractal and glcm features for mri and ct scan images cla...sipij
Ā
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale
complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of
irregularity of the medical images. This descriptor property does not give ownership of the local image
structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM
Features. This powerful combination has proved good results especially in classification of medical texture
from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical
diagnostics tests for osteoporosis pathologies.
Image retrieval and re ranking techniques - a surveysipij
Ā
There is a huge amount of research work focusing on the searching, retrieval and re-ranking of images in
the image database. The diverse and scattered work in this domain needs to be collected and organized for
easy and quick reference.
Relating to the above context, this paper gives a brief overview of various image retrieval and re-ranking
techniques. Starting with the introduction to existing system the paper proceeds through the core
architecture of image harvesting and retrieval system to the different Re-ranking techniques. These
techniques are discussed in terms of approaches, methodologies and findings and are listed in tabular form
for quick review.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
Ā
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of āYesā in the captured EEG, as opposed to a certain pattern of āNoā when the user is relaxed. Accordingly, the task is to determine that the captured EEG is āYesā or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
Ā
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of āYesā in the captured EEG, as opposed to a certain pattern of āNoā when the user is relaxed. Accordingly, the task is to determine that the captured EEG is āYesā or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
Ā
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of āYesā in the captured EEG, as opposed to a
certain pattern of āNoā when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is āYesā or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
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.
INHIBITION AND SET-SHIFTING TASKS IN CENTRAL EXECUTIVE FUNCTION OF WORKING ME...sipij
Ā
Understanding of neuro-dynamics of a complex higher cognitive process, Working Memory (WM) is
challenging. In WM, information processing occurs through four subsystems: phonological loop, visual
sketch pad, memory buffer and central executive function (CEF). CEF plays a principal role in WM. In this
study, our objective was to understand the neurospatial correlates of CEF during inhibition and set-shifting
processes. Thirty healthy educated subjects were selected. Event-Related Potential (ERP) related to visual
inhibition and set-shifting task was collected using 32 channel EEG system. Activation of those ERPs
components was analyzed using amplitudes of positive and negative peaks. Experiment was controlled
using certain parametric constraints to judge behavior, based on average responses in order to establish
relationship between ERP and local area of brain activation and represented using standardized low
resolution brain electromagnetic tomography. The average score of correct responses was higher for
inhibition task (87.5%) as compared to set-shifting task (59.5%). The peak amplitude of neuronal activity
for inhibition task was lower compared to set-shifting task in fronto-parieto-central regions. Hence this
proposed paradigm and technique can be used to measure inhibition and set-shifting neuronal processes in
understanding pathological central executive functioning in patients with neuro-psychiatric disorders.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
Ā
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of āYesā in the captured EEG, as opposed to a
certain pattern of āNoā when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is āYesā or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
Ā
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.
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Ā
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
EEG based Brain Computer Interface (BCI) establishes a new channel between human brain and the
surrounding environment in order to disseminate instructions to the outside world. It is based on the
recording of temporary EEG changes during different types of motor imagery such as imagination of
different hand movements. The spatial pattern of activated cortical areas during motor imagery is similar
to that of real time executed movement. Time domain features and frequency domain features are extracted
with emphasis on recognizing discriminative features representing EEG trials recorded during imagination
of different hand movements. Then, classification into different hand movements is carried out.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
Ā
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Ā
AbstractāThis 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.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Ā
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
Ā
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
Similar to ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING ELECTROENCEPHALOGRAPHY SIGNAL (20)
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Ā
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
Ā
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Ā
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Ā
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING ELECTROENCEPHALOGRAPHY SIGNAL
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
DOI : 10.5121/sipij.2013.4404 51
ANALYSIS OF BRAIN COGNITIVE STATE FOR
ARITHMETIC TASK AND MOTOR TASK USING
ELECTROENCEPHALOGRAPHY SIGNAL
R Kalpana1
, M Chitra2
, Navkiran Kalsi3
, Rajanikant Panda4
1
Dept. of Medical Electronics, B.M.S college of Engineering, Bangalore, India
2
Dept. of Information Science and Technology,
Sona College of Technology, Salem, India
3
Centre for Converging Technologies, University of Rajasthan, Jaipur, India
4
Dept. of Bio-Medical Engineering,
Trident Academy of Technology, Bhubaneswar, India
kalpana4research@gmail.com
ABSTRACT
To localize the brain dynamics for cognitive processes from EEG signature has been a challenging task
from last two decades. In this paper we explore the spatial-temporal correlations of brain electrical
neuronal activity for cognitive task such as Arithmetic and Motor Task using 3D cortical distribution
method. Ten healthy right handed volunteers participated in the experiment. EEG signal was acquired
during resting state with eyes open and eyes closed; performing motor task and arithmetic calculations.
The signal was then computed for three dimensional cortical distributions on realistic head model with
MNI152 template using standardized low resolution brain electromagnetic tomography (sLORETA). This
was followed by an appropriate standardization of the current density, producing images of electric
neuronal activity without localization bias. Neuronal generators responsible for cognitive state such as
Arithmetic Task and Motor Task were localized. The result was correlated with the previous neuroimaging
(fMRI study) investigation. Hence our result directed that the neuronal activity from EEG signal can be
demonstrated in cortical level with good spatial resolution. 3D cortical distribution method, thus, may be
used to obtain both spatial and temporal information from EEG signal and may prove to be a significant
technique to investigate the cognitive functions in mental health and brain dysfunctions. Also, it may be
helpful for brain/human computer interfacing.
KEYWORDS
EEG, sLORETA, Arithmetic Task, Motor Task, MNI152
1. INTRODUCTION
From past two decades to understand the brain for neuro-cognitive processes is the foremost query
to the cognitive neuroscience researchers. This is because of two main reasons: one, for brain
activity analysis that reveals how different brain regions interact with each other and second, the
current necessity to understand how our brains impact our perception of our environment and how
our brain function affects our behavior.
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
52
Electroencephalography (EEG) has an amazing capability to investigate the brain networks and
classify the brain state for cognitive function [1], [4], [6], [7], [8] and thus has been extensively
used for research and clinical purposes. EEG analyses for Cognitive Functions show us "how" our
brain is functioning on neuronal level with macroscopic scale [2], [5]. From previous studies it is
noted that the EEG can examine brain function with high temporal resolution but it has some
limitation- it doesnāt contain sufficient information on the three-dimensional (3D) distribution of
electric neuronal activity. On contrary, functional imaging techniques such as Positron emission
tomography (PET) and functional magnetic resonance imaging provide three-dimensional (3D)
images with an excellent spatial resolution, the temporal resolution is not high enough to match
with the speed at which neuronal processes occur. To solve the paradox, a growing number of
studies have been published that make use of functional imaging methods based on the
electroencephalogram (EEG) and the magnetoencephalogram (MEG) [1], [11]. The methods
proposed include quantitative spectral EEG analysis (qEEG), different computational algorithms
such as fast Fourier transform (FFT) or auto regressive (AR) models (Brenner et al., 1986; Coben
et al., 1985; Giaquinto and Nolfe, 1986; Prinz et al., 1992) Independent component analysis,
time-frequency (wavelet) analysis. But still many debate and query have there on brain dynamics
measurement for cognitive task using EEG signals.
In this paper, we examine the scalp EEG signal with spatial-temporal correlations for resting state
brain and cognitive state using 3D cortical distribution method. Ten healthy right handed
participants were taken for Electroencephalography (EEG) signal recording in resting eye open
and resting eye close (non-cognitive) and also while performing cognitive tasks such as motor
sensory task and arithmetic calculation task. The EEG signal was computed for three dimensional
cortical distributions on realistic head model using the MNI152 template using standardized low
resolution brain electromagnetic tomography (sLORETA) [9], [10], 14]. The brain networks were
examined with scalp and cortical level and the result was compared with the previous neuro
imaging research such as functional Magnetic Resonance Imaging (fMRI) study.
2. MATERIAL AND METHODS
2.1 Subjects Details
Ten adult right-handed healthy people with a mean age of 28 years (SD Ā± 5 years) participate for
electroencephalography (EEG) signal recording. The participants had no history of neurological or
psychiatric disease and did not take any medication that could affect the experiment. All
participants have more than five teen years of education and with IT (Information Technology)
professional skill.
2.2 EEG Data Acquisitions
The EEG cap consisted of 31 uni polar scalp electrodes placed according to the international 10-20
system electrode placement and one additional electrode dedicated to the vertical electrooculogram
(EOG) refer fig 1. Data were recorded relative to an FCz reference and a ground electrode was
located at Iz (10ā5 electrode system, (ostenveld and Praamstra, 2001). Data were sampled at 1000
Hz and the impedance between electrode and scalp was kept below 5 k . Data was acquired in a
close room with a comfort sit. The room was containing very minimum no of electronic gadget
and very good grounding. The paradigm instruction was given via small mike situated inside the
room. Inside the room one LCD monitor has there for visual representation.
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
53
Fig 1. International 10-20 system for electrode placement
2.3 Paradigm for Different Tasks
Three type of cognitive task was performed fallowed to eye open and eye close at relax resting
condition. Details are fallowed.
2.3.1 Instruction for eye open, close at rest
Subject was instructed to be sited with relax condition, as per instruction subjects was open their
eye for five minute and closed their eye for five minute. At that time instruction was given to do
not do any cognitive task like language, attention, memory related or motor tasks as much as
possible.
2.3.2 Instruction for Motor task
Subject was instructed four sections such as Relax, to squeeze right hand, relax, to squeeze left
hand. Each of section was thirty second and whole cycle was repeated three times.
2.3.3 Instruction for Arithmetic Calculation
Subject was instructed to count the Fibonacci sequence is the series of numbers: 0, 1, 1, 2, 3, 5, 8,
13, 21, 34ā¦ from 1 to 100.
3. DATA ANALYSIS
3.1 Pre Processing and Artifact Removal from EEG signal
Raw EEG data were processed offline using MATLAB software. The Data was down-sampled to
250 Hz. After that 4th order band-pass Butterworth FIR filter was applied with a cut-off frequency
of 0.01 to 60 Hz for attenuate low frequency noise (DC offset voltage, movement artifact) & high
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
54
frequency noise (environmental noise, instrumental noise), then 50 Hz notch filter was applied for
power line interference [3], [8]. After that baseline correction was done. Once artifacts had been
removed, the data were then inspected visually for artifacts resulting from muscular sources or any
other electro-physiological artifact and if any epochs containing voltage of more than 150 ĀµV was
manually rejected. After that the EEG data was taken for further study.
3.2 Post Processing for EEG Signal
On the basis of the scalp-recorded electric potential distribution, sLORETA (tomographic method)
was used to compute the cortical three-dimensional distribution of current density for all
segmented data sets (Resting Eye open, resting Eye close, motor task, attention and memory task).
Low resolution electromagnetic tomography (LORETA) assumes that the smoothest of all activity
distributions is most plausible (āāsmoothness assumptionāā) and therefore, a particular current
density (d) distribution is found [9]. This method followed by an appropriate standardization of the
current density, producing images of electric neuronal activity without localization bias.
Computations were made in a realistic head model using the MNI152 template [10] with the three-
dimensional solution space restricted to cortical gray matter. sLORETA images represent the
electric activity at each voxel in neuro anatomic Talairach space as the squared standardized
magnitude of the estimated current density.
To overcome the EEG inverse problem, a method is the dipole source with fixed locations and
orientations that are distributed in the whole brain volume or cortical surface. There are six
parameters that specify the dipole, three spatial coordinates (x, y, z) and three dipole moment
components (orientation angles (Īø, Ļ) and strength d). The sources are intracellular currents in the
dendritic trunks of the cortical pyramidal neurons, which are normally oriented to the cortical
surface [6], fixed orientation dipoles are generally set to be normally aligned. The amplitudes (and
direction) of these dipole sources are then estimated. Since the dipole location is not estimated the
problem is a linear one.
There are NE instantaneous extra cranial measurements in the surface of brain and NV voxels in the
brain. Typically, the voxels are determined by subdividing uniformly the solution space, which is
usually taken as the cortical grey matter volume or surface. At each voxel there is a point source,
which may be a vector with three unknown components (i.e., the three dipole moments), or a
scalar (unknown dipole amplitude, known orientation). The cases considered here correspond to
NV > > NE.
The Bayesian methods find an estimate D is
D= min (U (d)) ; where U(d)=||MāGd||2
R+Ī±L(d) (1)
G= Generalized Cross Validation, M is the matrix of data measurements at different times m (r, t)
and d is the matrix of dipole moments at different time instants.
The sLORETA uses the current density estimate given by the minimum norm estimate DMEN
(MNE-Minimum norm estimates) and standardizes it by using its variance, which is hypothesized
to be due to the actual source variance SD = I3p, and variation due to noisy measurements SM
noise=
Ī±IN. The electrical potential variance SM = GSDGT
+ SM
noise and the variance of the estimated
current density is SD=TMNESMTT
MNE=GT
[GGT
+Ī±IN]ā1
G. This is equivalent to the resolution matrix
TMNEG. For the case of EEG with unknown current density vector, sLORETA gives the following
estimate of standardized current density power:
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
55
DT
MNE,l {[SD]ll}ā1
DMNE,l (2)
where DMNE,l Ļµ R3 Ć 1
is the current density estimate at the l th voxel given by the minimum norm
estimate and [SD]ll Ļµ R3 Ć 3
is the l th diagonal block of the resolution matrix SD. It was found [16]
that in all noise free simulations, although the image was blurred, sLORETA had exact, zero error
localization when reconstructing single sources, that is, the maximum of the current density power
estimate coincided with the exact dipole location. In all noisy simulations, it had the lowest
localization errors when compared with the minimum norm solution and the Dale method [17].
The Dale method is similar to the sLORETA method in that the current density estimate given by
the minimum norm solution is used and source localization is based on standardized values of the
current density estimates. However, the variance of the current density estimate is based only on
the measurement noise, in contrast to sLORETA, which takes into account the actual source
variance as well.
4. RESULT
4.1 Resting state With Eye Open and Eye Close Condition
Resting state With Eye Open and Eye Close Condition as per neuroscience when a person with
relax and resting state and not involved with any physical and mental work, the activity occur in
the brain may random and unpredictable. In relax eye open and eye closed condition the frequency
range will be differ [13]. Awake and relax with eye open state lower beta range (10Hz to 14Hz)
frequency will be dominated, in other hand awake and relax with eye close condition alpha range
(7Hz to 11Hz) frequency will be dominated.
From our study we find out high variability in activated brain areas for awake and relax state in
both eye open and eye close condition. And we observe mostly occipital and frontal areas
activation at the time of resting state. In eye open condition we observe occipital lobe was
dominate with many other brain areas.
4.2 Complex Arithmetic Calculation Task
Number theory is a complex achievement of the human mind. However, the core concept of
arithmetic number is simple, and all human cultures have at least a few words for numbers. For
complex arithmetic tasks, the brain involved multifunction such as memory, planning and
executing and calculation. Also the sympathetic emotion involved at the time of complex
arithmetic tasks [12], [15].
From our study we find out for complex arithmetic tasks frontal lobe such as inferior prefrontal
cortex, right dorsa lateral prefrontal cortex and bilateral parietal lobe and cingulate brain areas was
activated. See the figure 2.
4.3 Sensory Motor Task
The sensory motor task is to understand sensation and motor activity such as movement.
In this study we find out the frontal lobe and parietal lobe are activated for Sensory Motor Task.
For most of the subject we got bilateral activation for both right hand and left hand motor task but
6. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
56
for right hand motto task we got left cerebellum dominate activation and for left hand motto task
we got right cerebellum dominate activation.
Fig 2. Neuronal activation in MNI template for complex arithmetic calculation task (Fibonacci sequence) computed
from EEG signal.
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
57
Fig 3. Neuronal activation in MNI template for right hand and left hand motor task computed from EEG signal.
5. CONCLUSION
To examine brain states for cognitive processes using EEG signal is a unique technique as it is
direct method to measure neuronal activity but it have the limitation on spatial resolution. We
examined brain networks from EEG signal using 3D cortical distribution method. This method
followed by an appropriate standardization of the current density, producing images of electric
neuronal activity without localization bias. Brain regions are significantly distinguished for
cognitive state such as arithmetic calculation task and motor task. The result was correlating with
8. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
58
the previous neuroimaging research (fMRI study) investigation. Hence this result indicates that,
the temporal and spatial dimensions of cortical activity from EEG signal can be demonstrated
using sLORETA making it an important and affordable tool for cognitive function analysis in
clinical neurosciences such as mental health, traumatic and acquired brain dysfunction or injury etc
also it may helpful for brain/human computer interfacing.
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9. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013
59
AUTHOURS PROFILE:
Mrs. R. KALPANA received B.E degree in Electronics and communication Engineering from Hindustan
college of Engineering, University of Madras, in 1992 and received M.Tech degree in Digital
Communications. from Department of Electronics, B.M.S College of Engineering, Visvesvaraya Institute of
Technology in 2005, Bangalore. She has 12 years of teaching experience in field of Biomedical Electronics.
Currently she is working as Asst. Prof in Department of Medical electronics. She has published 4
international research papers.
Prof. M. CHITRA received PhD Degree in Information and communication engineering from Anna
university of Technology Chennai, in the year 2009. She is currently working as Prof. Department of
Information science and Technology , Sona College of Technology. She has 15 years of teaching experience
in field of Information science and communication Engineering. Her Area of Interest is Mathematics, Neural
networks, communication and signal processing and Image processing. She has published more than 10
International research papers.
Ms. Navkiran Kalsi, received B.Tech and M.Tech degree in Cognitive Neuroscience from 3Centre for
Converging Technologies, University of Rajasthan, India, in 2012. She has 1 year of research experience on
Neuroimaging and EEG signal processing on cognation at National Institute of Mental Health and
Neurosciences, Bangalore, India. Currently she is working in a DST project at National Institute of Mental
Health and Neurosciences as a Research Fellow.
Mr. Rajanikant Panda received B.Tech degree in Biomedical Engineering from Trident Academy of
Technology, Biju Patnaik University of Technology (BPUT), in 2010. He has 1 year of Bio-Medical
Industrial Experience at Humankarigar Pvt. Ltd. and 2 year of research experience on multimodal
Neuroimaging and EEG signal processing from National Institute of Mental Health and Neurosciences
Bangalore, India. Currently he is working in a DST project at National Institute of Mental Health and
Neurosciences as a Research Fellow. He has published 8 international research papers.