Neurological Responses to Meditation with EEG Analysis Using Novel Empirical Fourier-Bessel Decomposition Approach

Abstract

Meditation is known for its positive impact on well-being. Electroencephalogram (EEG) signals have been studied in the literature to analyze these effects. Due to the non-stationary nature of EEG signals, Fourier transform-based methods are not suitable for extracting brain patterns. Thus, analyzing and detecting changes in brain patterns associated with the progression of meditation effects become challenging. To address them, we propose a novel method, empirical Fourier-Bessel decomposition (EFBD), which combines Fourier-Bessel series expansion with an adaptive zero-phase filter bank to extract meaningful modes. This study has used EEG signals from 23 participants during listening to the Rudram mantra (RM) using 10-channel electrodes. To investigate significant features, energy across rhythms (delta, theta, alpha, beta, and gamma) has been computed from EFBD-based decomposed modes. Further, a detailed assessment of brain responses of subjects has been performed using time-frequency distribution (TFD) and topographical maps before, during, and after listening to RM. In the analysis, the band energy in the delta, theta, and alpha rhythms increased significantly after listening to the RM, which indicates enhanced relaxation, improved concentration, and reduced anxiety. It is also validated using brain region-specific analysis with TFD and topographical maps, showing increased activity in the frontal and temporal brain regions. The proposed method can be an effective tool to analyze the impact of meditation on mental health issues for individual practice. © 2025 IEEE.