In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision.
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.
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.
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
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.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
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.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ 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 “Sehha & Mawid” 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 developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
Let’s master the digital toolkit to harness lifelong neuroplasticitySharpBrains
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
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
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.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
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.
Medical and pharmaceutical applications of mobile EEG (brain scanning)andfaulkner
Uses of inexpensive, personal, commercially-available, and portable EEG devices for medical research. Testing of new drugs, patient-specific drug selection, monitoring of patient progress, augmentation of treatments (via neurofeedback), prediction of 'attacks' in mental illnesses (e.g. panic disorder), and better diagnoses of neurological disorders.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
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.
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
Review:Wavelet transform based electroencephalogram methodsijtsrd
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
This webinar is part of a 2-hour monthly series hosted by the Neurotechnology Innovation Network: https://ktn-uk.org/health/neurotechnology/
Each webinar features expert speakers and focusses on a new development in a different technology area.
The third topic in this series is Dementia treatment using a biodesign approach. Dementia can have enormous effects, not only to those suffering but also family members and others
caring for them, but there are currently no effective therapies available. Neurotechnology offers a new way of treating dementia.
There is growing evidence that technologies such as deep brain stimulation and transcranial magnetic stimulation could help treat some of the effects of dementia and brain-computer interfaces are now able to detect the first signs of dementia years before symptoms appear.
In collaboration with UK Dementia Research Institute this webinar explores novel neurotechnologies to treat dementia, discuss barriers to adoption and new opportunities in the field.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
Teaching Techniques: Neurotechnologies the way of the future (Stotler, 2019)Jacob Stotler
Presenting alternative to drugs from nuerotechnologies and teaching about clinical use of neurothreapy and therapeutic effectiveness of biological aspects of the use of clinical technologies.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
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.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
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.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ 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 “Sehha & Mawid” 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 developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
Let’s master the digital toolkit to harness lifelong neuroplasticitySharpBrains
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
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
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.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
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.
Medical and pharmaceutical applications of mobile EEG (brain scanning)andfaulkner
Uses of inexpensive, personal, commercially-available, and portable EEG devices for medical research. Testing of new drugs, patient-specific drug selection, monitoring of patient progress, augmentation of treatments (via neurofeedback), prediction of 'attacks' in mental illnesses (e.g. panic disorder), and better diagnoses of neurological disorders.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
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.
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
Review:Wavelet transform based electroencephalogram methodsijtsrd
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
This webinar is part of a 2-hour monthly series hosted by the Neurotechnology Innovation Network: https://ktn-uk.org/health/neurotechnology/
Each webinar features expert speakers and focusses on a new development in a different technology area.
The third topic in this series is Dementia treatment using a biodesign approach. Dementia can have enormous effects, not only to those suffering but also family members and others
caring for them, but there are currently no effective therapies available. Neurotechnology offers a new way of treating dementia.
There is growing evidence that technologies such as deep brain stimulation and transcranial magnetic stimulation could help treat some of the effects of dementia and brain-computer interfaces are now able to detect the first signs of dementia years before symptoms appear.
In collaboration with UK Dementia Research Institute this webinar explores novel neurotechnologies to treat dementia, discuss barriers to adoption and new opportunities in the field.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
The seminar discus about affective computing. and emotion based computing,its objectives,components of emotion, psychological theories of emotion, A-V-S emotional model, Electroencephlography (EEG),
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
The detection and diagnosis of various neurological disorders are performed using different medical
devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though
significant progress had been made in the analysis of EEG for diagnosis of different neurological
disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the
EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG
patterns could be used for early detection of CP. Twenty children participated in this study out of which ten
were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded
from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15
minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm
in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak
at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of
spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz.
The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can
be used for early detection of spastic CP.
The research discussed in this paper is part of a pilot study in the use of wearable devices incorporating electroencephalogram (EEG) and heartrate sensors to sense for the emotional responses closely correlated to frustration when performing certain tasks. For this study the methodology used a combination of puzzle, arcade style game and a meditation apps to emulate a task based environment and detect frustration and satisfaction emotions. Preliminary results indicate that degree of task completion has an effect on emotions and can be detected by EEG and heartrate changes.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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
Current Status and Future Research Directions in Monitoring Vigilance of Indi...ijsc
Working in monotonous environment often causes lack of concentration or fatigue in an operator and many times such non-vigilance leads to accidents. Therefore early detection of fatigued state has become essential in monotonous working environments like driving vehicle, operating machines etc. Such fatigued state often gets developed gradually and can be identified by certain symptoms. Different types of symptoms help in modelling non-vigilance in different ways. This paper reviews and compares current status of research in modelling fatigue where fatigue is modelled using probabilistic models, machine learning models, finite state machine etc. The paper also presents possible future research directions in the same field like identifying non-fatigue non-vigilance mental states, extending non-vigilance monitoring for mass audience etc.
CURRENT STATUS AND FUTURE RESEARCH DIRECTIONS IN MONITORING VIGILANCE OF INDI...ijsc
Working in monotonous environment often causes lack of concentration or fatigue in an operator and many
times such non-vigilance leads to accidents. Therefore early detection of fatigued state has become
essential in monotonous working environments like driving vehicle, operating machines etc. Such fatigued
state often gets developed gradually and can be identified by certain symptoms. Different types of symptoms
help in modelling non-vigilance in different ways. This paper reviews and compares current status of
research in modelling fatigue where fatigue is modelled using probabilistic models, machine learning
models, finite state machine etc. The paper also presents possible future research directions in the same
field like identifying non-fatigue non-vigilance mental states, extending non-vigilance monitoring for mass
audience etc.
Emotions are an unstoppable and uncontrollable aspect of mental state of human. Some bad situations give
stress and leads to different sufferings. One can’t avoid situation but can have awareness when body feel
stress or any other emotion. It becomes easy for doctors whose patient is not in condition to speak. In that
case person’s physiological parameters are measured to decide emotional status. While experiencing
different emotion, there are also physiological changes taking place in the human body, like variations in
the heart rate (ECG/HRV), skin conductance (GSR), breathing rate(BR), blood volume pulse(BVP),brain
waves (EEG), temperature and muscle tension. These were some of the metrics to sense emotive coefficient.
This research paper objective is to design and develop a portable, cost effective and low power
embedded system that can predict different emotions by using Naïve Bayes classifiers which are based on
probability models that incorporate class conditional independence assumptions. Inputs to this system are
various physiological signals and are extracted by using different sensors. Portable microcontroller used
in this embedded system is MSP430F2013 to automatically monitor the level of stress in computer. This
paper reports on the hardware and software instrumentation development and signal processing approach
used to detect the stress level of a subject.To check the device's performance, few experiments were done in
which 20 adults (ten women and ten men) who completed different tests requiring a certain degree of effort,
such as showing facing intense interviews in office.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
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 “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” 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.
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer 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.
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Analysis of emotion disorders based on EEG signals ofHuman Brain
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
DOI : 10.5121/ijcsea.2012.2403 19
Analysis of emotion disorders based on EEG
signals of Human Brain
Ashish Panat 1
and Anita Patil 2
1
Priyadarshini Indira College of Engineering
asishpanat@gmail.com
2
Department of Electronics & Telecommunication, Cummins College of Engineering for
Women, Pune.
anita.patil&@cumminscollege.in
ABSTRACT
In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this
research is to study the analysis of the changes in the brain signals in the domain of different emotions. The
observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and
depression in economical way with higher precision.
KEYWORDS
EEG, EDF format, Feature extraction, Image classifiers, Emotions, Psychosomatic disorders, Normal and excited
brain, Anxiety and Depression
1. INTRODUCTION
A Human Brain is the organ that gives the person the capacity for art, language, rational thoughts
and moral judgments. It is also responsible for each individual's personality, movements,
memories, and his perception about the world. It is one of the body's biggest organs, consisting of
some 100 billion nerve cells that not only put together and highly coordinated physical actions but
regulate our unconscious body processes, such as digestion and breathing.
Emotions play a significant and powerful role in everyday life of human beings. Impulsive
emotions express an indication of psychosomatic disorders. These disorders are reflected as the
changes in the electrical activities and chemical activities in the brain. The changes can be
observed by capturing the brain signals and images.
Psychiatrists nowadays, have to deal with the patients with either of two prominent psychological
disorders, viz., Anxiety and Depression. Moreover, the patients are not ready to accept that the
symptoms they are suffering from are indicative of some psychological disorder. It becomes a
difficult job for the Psychiatrist, relatives of the patients and people around him to convince that
he needs to be treated.
The proposed research is expected to quantify the psychological health of the patient from his
EEG, as far as the two problems mentioned above, i.e. Anxiety and Depression are considered.
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
20
Anxiety disorder: The term anxiety disorder covers several different forms of abnormal and
pathological fear and anxiety. It covers four aspects of experiences an individual may have:
mental apprehension, physical tension, physical symptoms and dissociative anxiety [1]. Anxiety
disorder is divided into three types viz., anxiety disorder, phobic disorder, and panic disorder;
each has its own characteristics and symptoms. They also require different treatment. The
emotions present in anxiety disorders range from simple nervousness to bouts of terror. The
amygdala is central to the processing of fear and anxiety, and its function may be disrupted in
anxiety disorders.
Anxiety disorder is a pattern of constant worry and anxiety over many different activities and
events. Other symptoms include difficulty in concentrating, fatigue, irritability, restlessness, and
often becoming startled very easily [2]. A phobic disorder is a persistent fear of an object or
situation. Panic disorder is an anxiety disorder characterized by recurring severe panic attacks. It
may also include significant behavioral changes lasting long and ongoing worry about the
implications or concern[3].
Depression: Depression is a state of low mood and aversion to activity that can affect a person's
thoughts, behaviour, feelings and physical well-being [4]. Depressed people may feel sad,
anxious, empty, worthless, guilty, irritable, or restless. They may lose interest in activities that
once were pleasurable, experience loss of appetite or overeating, or problems concentrating,
remembering details or making decisions. They may even contemplate or attempt suicide.
Insomnia, excessive sleeping, fatigue, loss of energy, or aches, pains or digestive problems that
are resistant to treatment may be present. The signals of the brain in these situations can also be
utilized to study the emotions which can lead to great help in diagnosis of psychosomatic
disorders.
The research is conducted previously to analyse the emotions by looking at the physiological
aspects like users’ heart rate, skin conductance and pupil dilation.
2. LITERATURE SURVEY
In [5] & [6], Researchers Z. Khalili et al. have worked on Emotion detection using EEG and
peripherals signals as Galvanic skin resistance, Respiration, Blood pressure, and Temperature.
From these inputs, common set of features such as Mean, standard deviation and minimum and
maximum of the set of data are extracted. Their research is further extended to study the
improvement in the results of EEG by using correlation dimension.
In [7], the same researchers have explored on different modalities for emotion detection, such as,
Visual (facial expression), Auditory (pitch, loudness, etc.), tactile (heart rate, skin conductivity
etc.) and Brain signals(EEG).
In [8], Researchers have studied Brain activation during judgments of Positive emotions: Pride
and Joy. They have used fMRI images for this purpose. However, recording fMRI of a brain is
comparatively costly affair as compared to recording of EEG.
Researchers Arman Savran et al. [9] have also developed a technique for multimodal emotion
detection. They have used the modalities like fNIRS, face video and EEG signal.
3. BLOCK DIAGRAM
Figure(1) shows the block diagram of the system to acquire the signals from the brain, pre-
processing of the captured signals ( e.g. EEG in this study), extract the features after processing
the signal, classify the processed signal and analyse it for detection of emotion disorder.
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
21
Fig.(1) Block Diagram of the system for analysis of Brain signals.
Initially, the EEG of the Brain is captured by the standard method recognized worldwide as
International 10-20 system. In the Digital EEG system, these signals are first amplified and then
digitized. The rate of digitization may vary from 100 Hz to 20 kHz, depending on the capacity of
the system. Most commonly, the EEG signal captured from the EEG machine is available in the
EDF format (European Data Format). It must be first converted into .Wav format which is
suitable for processing. The signals are then filtered. The pass band of the filter depends on the
frequency of the interest for that particular signal, e.g. a low pass filter for Delta and Theta waves,
a band pass filter for alpha waves, and a high pass filter for gamma waves etc.
After filtering the signal, the features can be extracted, which can be compared and used for
further analysis.
4. CAPTURING THE SIGNAL
The fig.(2) shows the placement of the electrodes for capturing the EEG.
Fig (2) Placement of electrodes for EEG [12]
As per the International Standard, known as international 10-20 system, 19 electrodes are
connected to different locations on the scalp, which are salient points from the clinical point of
view, and one reference electrode is connected to ground. Whenever, the detailed study of EEG is
intended in case of some patients, or for the research purpose, the number of electrodes may
increase up to 256 also. In case of infants, i.e. neonatal EEG, the number can be decreased.
5. PREPROCESSING
The EEG signal from the EEG machine is available in the EDF format (European Data Format).
It is first converted into .Wav format which is suitable for processing. The software EDF2WAV is
used for this purpose. These WAV files can be directly referred into MATLAB programs, as
input data. The different frequency bands. The Fig. (3) shows the flowchart of different steps of
Pre-processing followed by feature extraction.
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
22
Fig.(3) Pre-processing and feature extraction
6. FEATURE EXTRACTION
After pre- processing, the following features are extracted from this signal: Mean, Standard
deviation, skewness, kurtosis, mean of absolute values of first difference of raw signals, mean of
absolute values of first difference of normalized signal [5]. The images of these signals can be
stored for further analysis. For feature extraction, basic mathematical formulae for mean, variance
etc. can be applied. Also, one can use the wavelet transform for this purpose.
Comparison of these features can be made to find the emotion disorder. As a first step towards
detection of emotional disorder, one obvious symptom, seizure, is identified by comparing EEGs
of a patient in normal condition & with seizure.
Seizure is one of the symptoms that may occur in a patient suffering from anxiety. The abnormal
activity of the brain in epileptic patient is known as seizure condition. This activity appears on the
screen of the EEG machine as waveforms of varying frequency and amplitude measured in
voltage. In this study, two features, viz., Mean and Variance are compared to detect the seizure
occurred in the patient.
7. RESULTS
The following snap shots from MATLAB result windows show the conversion of EDF file
(Fig.4) to .WAV file (Fig.5), band pass filter applied to theta wave Fig.(6).
Fig.(4) Signal extracted from .EDF file.
Fig.(5) Result of MATLAB code for EEG in .WAV format
Fig(6) Result of Band pass filter for Theta wave (using FDA tool)
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
23
: Normal : seizure
Fig.(7) Plot of Mean for Normal and seizure condition
: Normal : seizure
Fig.(8) Plot of Variance from EEG signal recorded for Normal and seizure condition
Result Table
Feature Accuracy
Mean 75 %
Variance 93%
Table 1: Comparison of results of two features, Mean and Variance
8. CONCLUSION
The study has proved the effective utility of economical and simple method of study of brain
using EEG for diagnosis the different emotion disorders viz., anxiety and depression. One of the
major symptoms, seizure is analysed in this study. The EEG signal in EDF format is converted
into .WAV format using EDF to WAV converter. The signal is then passed through the filters of
different frequencies to separate alpha, beta, delta and theta waves. The features are extracted
from these signals. After analyzing Mean, Power Spectral Entropy and Variance of both normal
and seizer EEG signals, it was found that using Variance gives more accurate information about
the seizure in EEG signal among the given feature sets.
This technique has revealed the possibility of precise diagnosis of psychosomatic disorders in
more simple and economical way.