Meditation-induced cognitive state classification using sparse brain connectivity features and electroencephalography

Brain-Computer Interface (BCI) technology offers potential for improving meditation practices via real-time neural feedback. Traditional EEG signal processing often fails to account for temporal and inter-channel relationships in the data. This study addresses the gap by modeling EEG signals using a multivariate auto-regressive (MVAR) approach, capturing both temporal dynamics and inter-channel interactions. Sparsity is introduced using the group least absolute shrinkage and selection operator (GLASSO), reducing volume conduction issues. From the sparse coefficient matrix, brain connectivity features such as average energy value (EV), phase lag value (PLV), mean absolute correlation (MAC) and magnitude squared coherence (MSC) are extracted. Statistical analyses and scatter plots highlight the influence of these features on cognitive states during meditation. EEG data is classified into EM, NM and CO states using decision trees (DT), Gaussian naive Bayes (GNB), k-nearest neighbor (KNN), and a multi-layer feed-forward neural network (MLFFNN). Metrics include precision, recall, F1-score and accuracy. DT achieved the best performance with 97.12% accuracy, 96.12% precision, 97.39% recall and an F1-score of 97.01%. This study enhances the EEG classification of meditation states by adding sparsity to the MVAR model. Future work could focus on real-time applications for feedback-driven meditation enhancement.