Decoding Cognitive Load Changes Induced by Mantra Meditation From Physiological Signals Using Deep Neural Networks
IEEE Sensors Journal November 14, 2025 Swati Singh, Jyotiranjan Beuria, Laxmidhar Behera et al. 4 citations
Mantra meditation reduces cognitive load and improves autonomic balance, as shown by physiological markers and deep learning analysis. In a study combining EEG-derived cognitive load indices (cognitive load quotient, neural efficiency index) and heart rate variability markers (coherence index, stress index), participants who meditated showed significant reductions in cognitive load quotient and stress index, alongside increases in neural efficiency and coherence indices. Deep learning models classifying cognitive states (pre, task, post) revealed opposite accuracy trends: control group accuracy improved (EEGNet-Attention: 93.27% to 97.67%; EEGNet: 91.89% to 95.04%), while experimental group accuracy declined (EEGNet-Attention: 92.09% to 86.67%; EEGNet: 89.83% to 78.04%). This post-meditation accuracy drop suggests meditation made EEG patterns less distinct across cognitive phases, reflecting reduced cognitive load and greater neurophysiological efficiency.