A Systematic Review of EEG Signal Processing and AI Driven Techniques in Brain–Computer Interface

Abstract

In the field of neuroscience, Meditation has gained significant focus because of its positive effects on cognitive performance and mental health. With the help of Brain Computer Interface (BCI), communication between the brain and other devices can be made possible through the analysis of brain signals. As Electroencephalography (EEG) is non-invasive in nature and has high temporal resolution, it is widely used in BCI applications. Meditative EEG signals have attracted attention because meditation alters brain activity patterns, which can be analyzed for cognitive and mental health applications in recent years. The accuracy of EEG signal classification and interpretation has significantly improved with advanced machine learning techniques. This paper reviews and explores the frameworks used for analyzing meditative EEG signals using machine learning algorithms. It discusses EEG signal acquisition, signal pre-processing methods, feature extraction techniques, and classification approaches. Moreover, the paper points out present challenges and potential areas for future research in creating reliable BCI systems for analyzing meditation.