Bioelectrics
Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii
Volume 14, Issue 3 , October 2020, , Pages 179-193
Abstract
In recent years, driver fatigue has become one of the major causes of road accidents, and many studies have been conducted to analyze driver fatigue. EEG signals are considered the most reliable method for measuring driver fatigue because of the non-invasive nature. Manual interpretation of EEG signals ...
Read More
In recent years, driver fatigue has become one of the major causes of road accidents, and many studies have been conducted to analyze driver fatigue. EEG signals are considered the most reliable method for measuring driver fatigue because of the non-invasive nature. Manual interpretation of EEG signals for detection of driver fatigue is impossible, so an automatic detection of driver fatigue from EEG signals should be provided. One of the problems regarding the automatic detection of driver fatigue is extraction and selection of discriminative features witch generally leads to computational complexity. This paper prepares a new approach to automatic classifying 2 stages of driver fatigue from 6 active regions of EEG signals. In the proposed method, directly apply the raw EEG signal to convolutional neural network-long short time memory (CNN-LSTM) network, without involving feature extraction/selection. This is a challenging process in previous literature. The proposed network architecture includes 7 convolutional layers with 3 LSTM layers followed by 2 fully connected layers. The LSTM network in a fusion with the CNN network has been used to increase stability and reduce oscillation. The simulation results of the proposed method for classifying 2 stages of driver fatigue for 6 active regions A, B, C, D, E (based single-channel) and F show the accuracy of 99.23%, 97.55%, 98.00%, 97.26%, 98.78%, 93.77% and Cohen’s Kappa coefficient of 0.98, 0.96, 0.97, 0.96, 0.98 and 0.92 respectively. Furthermore, comparing the obtained results with the previous methods reveals the performance improvement of the proposed driver fatigue detection in terms of accuracy. According to the high accuracy of the proposed single-channel (region E) method, it can be used for the design of automatic detection of driver fatigue systems with high speed and accuracy.
Bioelectrics
Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii
Volume 14, Issue 3 , October 2020, , Pages 209-220
Abstract
Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of ...
Read More
Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of epilepsy is extraction of desirable features which can make the most distinction between different stages of epilepsy. The process of finding the proper features is generally time consuming. This study presents a new approach for the automatic identification of different epileptic stages. In this paper, a sparse represantion-based classification (SRC) with proposed dictionary learning is used to automatically identify the different stages of epilepsy using the EEG signal. The proposed method achieves 100% accuracy, sensitivity and specificity in 8 out of 9 scenarios. Also the proposed algorithm is resistant to Gaussian noise up to 0 decibels. The results show that using the proposed algorithm to identify different epileptic stages has a higher success rate than other similar methods.
Seyedeh Saeideh Zahedi Haghighi; Sayed Mahmoud Sakhaei; Mohammadreza Daliri
Volume 13, Issue 2 , August 2019, , Pages 95-104
Abstract
Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary ...
Read More
Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary property of EEG, intrinsic mode functions (IMF) extracted by using empirical mode decomposition (EMD) and then first 3 IMFs selected. Each IMF converts into smaller pieces with a one-second window and power feature has been extracted from each piece. Then, by using a suitable mapping, the position of the electrodes in the 10-20 system becomes the position of the pixels in the picture. The extracted features are considered as pixel color components. To determine the class of valence, the set of all generated pictures is given as input to a deep learning network and output determine the high or low class of valence. The same process is used to determine the class of arousal. To examining the method, the DEAP dataset is used. By choosing 17×17 for the image size, the mean accuracy and standard deviation were obtained of 78.58% and 3.9 for the valence and 78.66% and 3.1 for the arousal which that shows a significant improvement compared to similar tasks.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Nasrin Shourie; Seyed Mohammad Firouzabadi; Kambiz Badie
Volume 7, Issue 4 , June 2013, , Pages 321-331
Abstract
In this article, differences between multichannel EEG signals of artists and nonartists were investigated during visual perception and mental imagery of some paintings and at resting condition using scaling exponent. It was found that scaling exponent is significantly higher for artists as compared to ...
Read More
In this article, differences between multichannel EEG signals of artists and nonartists were investigated during visual perception and mental imagery of some paintings and at resting condition using scaling exponent. It was found that scaling exponent is significantly higher for artists as compared to nonartists during the three mentioned states, suggesting that scaling exponent may reflect the influence of artistic expertise. No significant difference in scaling exponent was observed between the visual perception and the mental imagery tasks. In addition, the two groups were classified using scaling exponent of channel C4 and Neural Gas classifier during the visual perception, the mental imagery and the resting condition. The average classification accuracies were 50%, 58.12% and 70%, respectively. The obtained results suggest that discriminability in scaling exponent decreases during the performance of similar cognitive tasks.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mehdi Abdossalehi; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi
Volume 7, Issue 2 , June 2013, , Pages 143-153
Abstract
In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving ...
Read More
In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving the illposeness problem of extracted components by ICA algorithm, first these sources were sorted by Shannon entropy and then the features of Katz fractal dimension and the first local minimum of the mutual information based on the time delay (tau) have been extracted for representing determinism. The results show that the determinism ratio of the sorted sources has significant difference during the time in three emotional states: positive, negative and neutral. The determinism ratio increases in neutral, negative and positive emotional states, respectively.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Shahab Shahvazian; Vahid Abootalebi; Mohammad Taghi Sadeghi
Volume 6, Issue 1 , June 2012, , Pages 35-47
Abstract
With the advent of biometric knowledge, conventional methods of authentication are being replaced with biometric based methods. Recently, the use of EEG signal in biometric systems attracted increasing research attention. Only a few works have been done in this emerging of EEG-based biometry mainly focusing ...
Read More
With the advent of biometric knowledge, conventional methods of authentication are being replaced with biometric based methods. Recently, the use of EEG signal in biometric systems attracted increasing research attention. Only a few works have been done in this emerging of EEG-based biometry mainly focusing on person identification not on person authentication. This paper examines the effectiveness of the EEG as a biometric for person authentication. In this study, the EEG signal from fifteen volunteer recorded during imagination of opening and closing fist was used. A set of AR coefficients, power of spectral bands, Energy Spectral Density, Energy Entropy and Sample Entropy were used as extracted features. The authentication system is fused at the sensor module and features to support a system which can meet more challenging and varying requirements. The utility of the sequential search methods is also experimentally studied. In the extensive experimentation on the Shalk and his colleague’s database, we demonstrate that with combination of features when using single channel EEG, the performance of system is improved in two ways of single block and multi block methods compared to other. Result of this study shows a clear vision of commercial and practical use of the brain's electrical signals in the authentication systems of future.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Ali Khadem; Gholam Ali Hossein-Zadeh
Volume 6, Issue 1 , June 2012, , Pages 57-69
Abstract
Exploring the causal (delayed) brain relations is an important topic in the Neuroscience. The traditional estimators of brain causal (delayed) relations are mainly model-based and put restrictive assumptions on the brain dynamics. In the recent years, some nonparametric measures have been introduced ...
Read More
Exploring the causal (delayed) brain relations is an important topic in the Neuroscience. The traditional estimators of brain causal (delayed) relations are mainly model-based and put restrictive assumptions on the brain dynamics. In the recent years, some nonparametric measures have been introduced to solve this problem. Among them, the most important one is Transfer Entropy (TE) which is based on the information theory and Conditional Mutual Information concept. However, in the presence of significant instantaneous relations that are observed extensively in the brain functional datasets, TE may estimate the causal (delayed) relations inaccurately. In this paper, two information theoretic based measures called Instantaneous Interaction (II) and Modified Transfer entropy (MTE) are introduced to estimate the instantaneous and causal (delayed) brain relations, respectively. MTE is used instead of TE whenever II is significant. These measures are evaluated on 3 simulated models and eyes-closed resting state EEG data. The simulation results show high ability of II to estimate the linear and nonlinear instantaneous relations. Also, based on the simulation results MTE outperforms TE to estimate causal (delayed) relations in presence of significant instantaneous relations (significant II). For the real EEG data, II detects a significant instantaneous relation between Posterior and Frontal EEG channels. Also MTE detects the information flow from Posterior EEG channels to Frontal ones more significantly than TE does. So in presence of significant instantaneous relations in the real EEG data, MTE outperforms TE.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hosna Ghandeharion; Abbas Erfanian Omidvar
Volume 3, Issue 3 , June 2009, , Pages 199-211
Abstract
Contamination of Electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent Component Analysis (ICA) is now a widely accepted tool for detection of artifact in EEG data. This component-based method segregates artifactual activities ...
Read More
Contamination of Electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent Component Analysis (ICA) is now a widely accepted tool for detection of artifact in EEG data. This component-based method segregates artifactual activities in separate sources hence, the reconstruction of EEG recordings without these sources leads to artifact reduction. Identification of the artifactual components is a major challenge to artifact removal using ICA is the. Although, during past several years, it has been proposed for automatic detecting the artifactual component, there is still little consensus on criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on statistics and time-frequency properties of independent components for fully automatic ocular artifact suppression. By comparing the statistics and time-frequency properties of independent components, the artifactual components were identified and removed. The results on 2000 4-s EEG epochs indicate that the artifact components can be identified with an accuracy of 92.8%. Moreover, statistical test indicates that the statistics and time-frequency properties of artifactual components are significantly different from that of non-artifactual components.
