Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern, linear discriminant analysis, and LSTM

Abstract

This study proposes an approach to classify the EEG into meditation and non-meditation segments using a long short-term memory (LSTM) based deep neural network (DNN) framework. Inter-subject classification performance is assessed on EEG recorded from fourteen long-term Rajayoga meditators. Common spatial pattern is used for feature extraction, and linear discriminant analysis is used for dimensionality reduction. The sequence of features thus obtained is fed to a LSTM based DNN, which employs a fully connected layer for classification. We have achieved inter-subject classification accuracies of 79.1 %, 86.5%, 91.0%, and 94.1% with the respective use of the alpha, beta, lower-gamma, and higher-gamma bands for classification. To the best of our knowledge, this is the first work to employ deep learning to distinguish between the brain's electrical activity during meditation and at rest.