Human Cognitive Behavior and Brain State analysis in different physiological conditions using EEG signal: A systematic review

Researchers use EEG signal processing to detect brain states and understand human cognitive behavior. It involves analyzing the electrical signals generated by the brain to identify specific patterns that indicate different brain states and cognitive activities. With its unparalleled precision, EEG can rapidly and non-invasively recognize changes in mental state and cognitive performance. This method provides insights into brain operations at a millisecond precision, faster than other neuroimaging modalities, such as fMRI or PET. This paper provides a comprehensive analysis of the utilization of EEG signals for identifying various brain states and cognitive tasks using advanced analysis methods like machine learning and deep learning. The research is the survey outcome of high-impact articles, reports, and studies published in brain state activity and human cognitive behavior detection. The analysis shows the promising potential of using EEG signals for applications such as driving stress, work stress, driver fatigue, sleepiness, and mental states such as relaxed, neutral, highly concentrated, meditative, and non-meditative. The survey highlights the different types of publicly available EEG datasets, different feature extraction techniques, and the application of different machine learning and deep learning classifiers for EEG signal processing in cognitive action classification. Finally, we identify some research gaps and discuss possible future research directions related to brain state and cognitive action detection.