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

Panachakel, JT and Pradeep Kumar, G and Ramakrishnan, GA and Sharma, K (2022) Automated classification of EEG into meditation and non-meditation epochs using common spatial pattern, linear discriminant analysis, and LSTM. In: 2021 IEEE Region 10 Conference, TENCON, 7-10 Dec 2021, Auckland, pp. 215-218.

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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. © 2021 IEEE.

Item Type:	Conference Paper
Publication:	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Brain; Deep neural networks; Discriminant analysis, Automated classification; Common spatial patterns; Deep learning; LDA; Linear discriminant analyze; Meditative state; Network frameworks; Rajayoga meditation; Resting state; Subject classification, Long short-term memory
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	16 May 2022 06:28
Last Modified:	16 May 2022 06:32
URI:	https://eprints.iisc.ac.in/id/eprint/71670

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