Alexandra Piryatinska1, Boris Darkhovsky2, Alexander Kaplan3. 1. Department of Mathematics, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94070, United States. Electronic address: alpiryat@sfsu.edu. 2. Institute for Systems Analysis, FRC CSC RAS, Higher School of Economics, Moscow, Russia. 3. Department of Human Physiology, M.V. Lomonosov Moscow State University, Pirogov Russian National Research Medical University, Moscow, Russia.
Abstract
BACKGROUND AND OBJECTIVE: A crucial step in a classification of electroencephalogram (EEG) records is the feature selection. The feature selection problem is difficult because of the complex structure of EEG signals. To classify the EEG signals with good accuracy, most of the recently published studies have used high-dimensional feature spaces. Our objective is to create a low-dimensional feature space that enables binary classification of EEG records. METHODS: The proposed approach is based on our theory of the ϵ-complexity of continuous functions, which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle multichannel-EEG records. The method consists of two steps. Firstly, we estimate the ϵ-complexity coefficients of the original signal and its finite differences. Secondly, we utilize the random forest (RF) or support vector machine (SVM) classifier. RESULTS: We demonstrated the performance of our method on simulated data. We also applied it to the problem of classification of multichannel-EEG records related to a group of healthy adolescents (39 subjects) and a group of adolescents with schizophrenia (45 subjects). We found that the random forest classifier provides a superior result. In particular, out-of-bag accuracy in the case of RF was 85.3%. Using 10-fold cross-validation (CV), RF gave an average accuracy of 84.5% on a test set, whereas SVM gave an accuracy of 81.07%. We note that the highest accuracy on CV was 89.3%. To compare our method with the classical approach, we performed classification using the spectral features. In this case, the best performance was achieved using seven-dimensional feature space, with an average accuracy of 83.6%. CONCLUSIONS: We developed a model-free method for binary classification of EEG records. The feature space was reduced to four dimensions. The results obtained indicate the effectiveness of the proposed method.
BACKGROUND AND OBJECTIVE: A crucial step in a classification of electroencephalogram (EEG) records is the feature selection. The feature selection problem is difficult because of the complex structure of EEG signals. To classify the EEG signals with good accuracy, most of the recently published studies have used high-dimensional feature spaces. Our objective is to create a low-dimensional feature space that enables binary classification of EEG records. METHODS: The proposed approach is based on our theory of the ϵ-complexity of continuous functions, which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle multichannel-EEG records. The method consists of two steps. Firstly, we estimate the ϵ-complexity coefficients of the original signal and its finite differences. Secondly, we utilize the random forest (RF) or support vector machine (SVM) classifier. RESULTS: We demonstrated the performance of our method on simulated data. We also applied it to the problem of classification of multichannel-EEG records related to a group of healthy adolescents (39 subjects) and a group of adolescents with schizophrenia (45 subjects). We found that the random forest classifier provides a superior result. In particular, out-of-bag accuracy in the case of RF was 85.3%. Using 10-fold cross-validation (CV), RF gave an average accuracy of 84.5% on a test set, whereas SVM gave an accuracy of 81.07%. We note that the highest accuracy on CV was 89.3%. To compare our method with the classical approach, we performed classification using the spectral features. In this case, the best performance was achieved using seven-dimensional feature space, with an average accuracy of 83.6%. CONCLUSIONS: We developed a model-free method for binary classification of EEG records. The feature space was reduced to four dimensions. The results obtained indicate the effectiveness of the proposed method.