Saswati Sanyal Choudhury

2 papers in the library · publishing 2025-2026

Papers

A deep learning approach for analyzing brainwave signals during audio meditation in expectant mothers: variational mode decomposition with a CNN-BiLSTM model.

Biomedical physics & engineering express March 31, 2026 Daisy Das, Nabamita Deb, Saswati Sanyal Choudhury

Brief audio meditation measurably alters brain activity in pregnant women. EEG signals recorded during resting, meditation, and post-meditation states were analyzed using Variational Mode Decomposition and a hybrid deep learning model combining CNNs and Bidirectional LSTM networks. The model classified the three mental states with 94.79% test accuracy, demonstrating that short-term cognitive modulation can be detected reliably. The findings provide an objective framework for evaluating meditation's neural effects during pregnancy, supporting its potential as a non-pharmacological intervention for stress, anxiety, and depression in maternal care.

DOI: 10.1088/2057-1976/ae55a8 Full text Meditation Cnn EEG Lstm +2

Evaluating Brain Activity in Response to Short Meditation Stimuli Using R-Score, Electroencephalography, and Neural Networks.

Annals of Neurosciences October 17, 2025 Daisy Das, Nabamita Deb, Rita Rani Talukdar et al.

Short audio interventions can induce relaxation in pregnant women, as measured by EEG brain activity. A new classification model combining EEG data with an artificial neural network achieved 100% accuracy in identifying meditative states during mantra meditation, particularly using signals from the frontal midline, right frontal lobe, and parietal lobe. The study emphasizes that EEG readings during meditation are more significant than those after meditation, and it offers insights into trimester-specific responses to brief audio stimuli, suggesting such interventions may be useful for stress reduction in prenatal care.

DOI: 10.1177/09727531251379925 Full text Brief Audio Electroencephalography EEG Machine learning +2