Subject-independent Classification of Meditative State from the Resting State using EEG

• URL: http://arxiv.org/abs/2504.18095v1
• Date: Fri, 25 Apr 2025 05:44:51 GMT
• Title: Subject-independent Classification of Meditative State from the Resting State using EEG
• Authors: Jerrin Thomas Panachakel, Pradeep Kumar G., Suryaa Seran, Kanishka Sharma, Ramakrishnan Angarai Ganesan,
• Abstract summary: This study aims to distinguish the modified state of consciousness experienced during Rajyoga meditation from the resting state of the brain in a subject-independent manner using EEG data.<n>The CSP-LDA-LSTM architecture gives the best performance with 98.2% accuracy for intra-subject classification.<n>The SVD-NN architecture provides significant performance with 96.4% accuracy for inter-subject classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While it is beneficial to objectively determine whether a subject is meditating, most research in the literature reports good results only in a subject-dependent manner. This study aims to distinguish the modified state of consciousness experienced during Rajyoga meditation from the resting state of the brain in a subject-independent manner using EEG data. Three architectures have been proposed and evaluated: The CSP-LDA Architecture utilizes common spatial pattern (CSP) for feature extraction and linear discriminant analysis (LDA) for classification. The CSP-LDA-LSTM Architecture employs CSP for feature extraction, LDA for dimensionality reduction, and long short-term memory (LSTM) networks for classification, modeling the binary classification problem as a sequence learning problem. The SVD-NN Architecture uses singular value decomposition (SVD) to select the most relevant components of the EEG signals and a shallow neural network (NN) for classification. The CSP-LDA-LSTM architecture gives the best performance with 98.2% accuracy for intra-subject classification. The SVD-NN architecture provides significant performance with 96.4\% accuracy for inter-subject classification. This is comparable to the best-reported accuracies in the literature for intra-subject classification. Both architectures are capable of capturing subject-invariant EEG features for effectively classifying the meditative state from the resting state. The high intra-subject and inter-subject classification accuracies indicate these systems' robustness and their ability to generalize across different subjects.

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