Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern, linear discriminant analysis, and LSTM
IEEE Region 10 Conference December 7, 2021 J. T. Panachakel, G. P. Kumar, A. G. Ramakrishnan et al.
A deep neural network using long short-term memory (LSTM) architecture can classify EEG recordings into meditation and non-meditation segments. The approach was tested on data from fourteen long-term Rajayoga meditators. Using common spatial pattern for feature extraction and linear discriminant analysis for dimensionality reduction, the network achieved inter-subject classification accuracies of 79.1% with the alpha band, 86.5% with beta, 91.0% with lower-gamma, and 94.1% with higher-gamma bands. This is the first work to apply deep learning to distinguish brain electrical activity during meditation from resting activity.