A. G. Ramakrishnan

2 papers in the library · publishing 2021-2023

Papers

Coherence-based interhemispheric EEG functional connectivity changes in distinct frequency bands during eyes open meditation

bioRxiv (Cold Spring Harbor Laboratory) February 6, 2023 G Pradeep Kumar, Kanishka Sharma, A Adarsh et al. preprint

Long-term Brahmakumaris Rajyoga meditators practicing with open eyes show distinct changes in brain functional connectivity compared to controls listening to music. Using two measures—magnitude squared coherence (MSC) and imaginary part of coherency (ICoh)—the study finds that meditators have higher baseline MSC in frontocentral and centroparietal regions and higher |ICoh| globally in higher beta and gamma bands. During meditation, MSC increases in higher theta and alpha bands in frontal and parietal regions, while |ICoh| decreases across most regions and bands except alpha. Controls show no such changes during music. Increased frontal MSC and decreased |ICoh| suggest enhanced self-awareness; lower occipital MSC in meditators at baseline implies altered visual processing from long-term open-eye practice.

DOI: 10.1101/2023.02.06.527252 Full text Meditation Active listening Electroencephalography Audiology +9

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.

DOI: 10.1109/tencon54134.2021.9707427 Computer science Medicine