Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern, linear discriminant analysis, and LSTM
J. T. Panachakel, G. P. Kumar, A. G. Ramakrishnan, Kanishka Sharma
IEEE Region 10 Conference December 7, 2021 DOI: 10.1109/tencon54134.2021.9707427 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Preprint Peer reviewed |
|---|---|
| Sample size | 14 |
| Population | Long-term Rajayoga meditators |
| Topics | Meditation |
| Key points | An LSTM-based deep neural network can classify EEG recordings into meditation and non-meditation segments with accuracies ranging from 79.1% to 94.1% depending on the frequency band used. |
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.