Meditation is a widely recognized practice that enhances mental well-being and cognitive function. Despite advances in EEG meditation neuroscience, challenges persist in extracting robust and interpretable features from complex, non-stationary EEG signals. Existing classification methods often rely on limited feature sets and traditional machine learning approaches. These methods lack...
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...