EEG-Based Brain-Computer Interface for Recognition of Human Brain Activity During Yoga and Meditation

A proposed EEG-based Brain Computer Interface (BCI) framework aims to recognize brain activity during yoga and meditation by analyzing neural oscillations. The methodology involves acquiring EEG signals from multiple scalp regions during various postures, removing artifacts with Independent Component Analysis, and extracting time-frequency features like band power in delta, theta, alpha, beta, and gamma bands. Principal Component Analysis reduces feature dimensionality, and machine learning algorithms such as Support Vector Machines, Random Forests, and EEGNet classify mental states. Performance is evaluated using accuracy, F1-score, and confusion matrices. The framework could enable real-time neurofeedback for mindfulness and focus, and be integrated into clinical neurorehabilitation and cognitive training systems.

Study at a glance

Abstract

Yoga and meditation have long been recognized for their profound impact on mental health, emotional regulation, and cognitive performance. However, understanding the underlying neural mechanisms that govern these altered states of consciousness remains a key research challenge. This chapter proposes an EEG-based Brain Computer Interface (BCI) framework for human brain activity recognition during yoga and meditation practices, focusing on cognitive and neurophysiological state characterization through advanced signal processing and machine learning techniques. The proposed methodology involves the acquisition of EEG signals from multiple scalp regions during various yoga and meditative postures, followed by preprocessing using Independent Component Analysis (ICA) to remove artifacts. Subsequently, time–frequency domain features such as band power (delta, theta, alpha, beta, and gamma) are extracted, capturing the neural oscillations associated with relaxation and attentional control. These features are then reduced via Principal Component Analysis (PCA) for dimensionality optimization. To classify mental states, several machine learning and deep learning algorithms, including Support Vector Machines (SVM), Random Forests, and EEGNet, are employed. The system's performance is evaluated using accuracy, F1-score, and confusion matrix metrics, ensuring reliable cognitive state recognition. The scope of this work extends to developing adaptive BCI models capable of providing real-time neurofeedback for enhancing mindfulness, focus, and emotional balance. Furthermore, the framework can be integrated into clinical neurorehabilitation and cognitive training systems, contributing to personalized therapy and mental health monitoring. By bridging traditional mindfulness practices with modern neurotechnology, this research advances the frontiers of non-invasive BCIs for cognitive enhancement, neurofeedback, and holistic well-being.