Brain Connectivity Based Classification of Meditation Expertise

Abstract

Recent developments in neurotechnology effectively utilize the decades of neuroscientific findings of multiple meditation techniques. Meditation is linked to higher-order cognitive processes, which may function as a scaffold for cognitive control. In line with these developments, we analyze oscillatory brain activities of expert and non-expert meditators from the Himalayan Yoga tradition. We exploit four dimensions (Temporal, Spectral, Spatial and Pattern) of EEG data and present an analysis pipeline employing machine learning techniques. We discuss the significance of different frequency bands in relation with distinct primary 5 scalp brain regions. Functional connectivity networks (PLV) are utilized to generate features for classification between expert and non-expert meditators. We find (a) higher frequency \(\beta \) and \(\gamma \) oscillations generate maximum discrimination over the parietal region whereas lower frequency \(\theta \) and \(\alpha \) oscillations dominant over the frontal region; (b) maximum accuracy of over 90% utilizing features from all regions; (c) Quadratic Discriminant Analysis surpasses other classifiers by learning distribution for classification. Overall, this paper contributes a pipeline to analyze EEG data utilizing various properties and suggests potential neural markers for an expert meditative state. We discuss the implications of our research for the advancement of personalized headset design that rely on feedback on depth of meditation by learning from expert meditators.