Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation

Abstract

Human-computer interaction investigates how people learn from technology, and how they use technology in everyday life. Researchers have used brain-computer interfaces to understand how technology can be designed to support human cognition and behavior. The most famous and consumer-friendly approach to measuring brain signals is electroencephalography (EEG) due to its non-invasive, portable, relatively inexpensive, and high temporal resolution. In this study, we develop machine learning models to distinguish between the neural oscillations of meditators and non-meditators. Previous studies have used power spectrum density, entropy, and functional connectivity to distinguish various meditation traditions. We use EEG data set comprising neural activity of expert meditators of Himalayan Yoga (HYT), Vipassana (VIP), Isha Shoonya (SYN), and non-expert control subjects (CTR). We analyze the data using 13 different machine learning models for within-subject and cross-subject. We present the results for six classification conditions for both meditation and mind-wandering. Features extracted from the mean of 64 EEG time series are fed into machine learning classifiers during training. We obtain maximum accuracy for within-subject classification in both meditation and mind-wandering. In cross-subject analysis, we obtained 18.3% above chance level in meditation between control and Isha Shoonya, and similarly above 18% chance level in mind-wandering between control and Vipassana. We discuss the implications of this result for the emerging consumer EEG headset facilitating meditation practice. Our results indicate that personalized models (within-subject) and generalized models (cross-subject) could guide naive (beginner) practitioners to meditate and aim to modulate brain signals by practicing to reach the expert level.