This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used a...This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns （TRCSP） is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.展开更多
Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on...Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.展开更多
Objective Classifying motor imagery tasks via functional near-infrared spectroscopy(fNIRS)poses a significant challenge in brain-computer interface(BCI)research due to the high-dimensional nature of the signals.This s...Objective Classifying motor imagery tasks via functional near-infrared spectroscopy(fNIRS)poses a significant challenge in brain-computer interface(BCI)research due to the high-dimensional nature of the signals.This study aimed to address this challenge by employing the common spatial pattern(CSP)algorithm to reduce input dimensions for support vector machine(SVM)and linear discriminant analysis(LDA)classifiers.Methods Data were collected from 15 healthy right-handed volunteers performing tasks involving left-hand motion,left-hand motor imagery,right-hand motion,and right-hand motor imagery.Signals from 20-channel fNIRS were utilized,with input features including statistical descriptors such as mean,variance,slope,skewness,and kurtosis.The CSP algorithm was integrated into both SVM and LDA classifiers to reduce dimensionality.The main statistical methods included classification accuracy assessment and comparison.Results Mean and slope were found to be the most discriminative features.Without CSP,SVM and LDA classifiers achieved average accuracies of 59.81%±0.97%and 69%±11.42%,respectively.However,with CSP integration,accuracies significantly improved to 81.63%±0.99%and 84.19%±3.18%for SVM and LDA,respectively.This value represents an increase of 21.82%and 15.19%in accuracy for SVM and LDA classifiers,respectively.Dimensionality reduction from 100 to 25 dimensions was achieved for SVM,leading to reduced computational complexity and faster calculation times.Additionally,the CSP technique enhanced LDA classifier accuracy by 3.31%for both motion and motor imagery tasks.Conclusion Integration of the CSP algorithm may demonstrate promising potential for improving BCI systems'performance.展开更多
基金The National Natural Science Foundation of China(No.61375118)the Program for New Century Excellent Talents in University of China(No.NCET-12-0115)
文摘This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns （TRCSP） is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.
文摘Objective Classifying motor imagery tasks via functional near-infrared spectroscopy(fNIRS)poses a significant challenge in brain-computer interface(BCI)research due to the high-dimensional nature of the signals.This study aimed to address this challenge by employing the common spatial pattern(CSP)algorithm to reduce input dimensions for support vector machine(SVM)and linear discriminant analysis(LDA)classifiers.Methods Data were collected from 15 healthy right-handed volunteers performing tasks involving left-hand motion,left-hand motor imagery,right-hand motion,and right-hand motor imagery.Signals from 20-channel fNIRS were utilized,with input features including statistical descriptors such as mean,variance,slope,skewness,and kurtosis.The CSP algorithm was integrated into both SVM and LDA classifiers to reduce dimensionality.The main statistical methods included classification accuracy assessment and comparison.Results Mean and slope were found to be the most discriminative features.Without CSP,SVM and LDA classifiers achieved average accuracies of 59.81%±0.97%and 69%±11.42%,respectively.However,with CSP integration,accuracies significantly improved to 81.63%±0.99%and 84.19%±3.18%for SVM and LDA,respectively.This value represents an increase of 21.82%and 15.19%in accuracy for SVM and LDA classifiers,respectively.Dimensionality reduction from 100 to 25 dimensions was achieved for SVM,leading to reduced computational complexity and faster calculation times.Additionally,the CSP technique enhanced LDA classifier accuracy by 3.31%for both motion and motor imagery tasks.Conclusion Integration of the CSP algorithm may demonstrate promising potential for improving BCI systems'performance.
