Development of efficient algorithm to detect and classify alpha activity in real-time EEG signal using LabVIEW for mental state detection

摘要

The paper proposes an algorithm to process Alpha activity in real-time electroencephalogram signal using LabVIEW for mental state detection. The electroencephalogram signals from occipital region have direct relationship with the state of mind which can be utilized by physically challenged individuals to control their surrounding environment, providing an extent of independency. Being independent reduces the stress level which results in a boost in both neural system as well as the immune system, giving a higher chance of recovery and preventing the physically challenged individuals from further sicknesses. The relaxed state of mind exhibits pronounced alpha activity which is continuously varying and appears as aperiodic boosts. The projects aims to efficiently detect alpha activity in real-time electroencephalogram signal for mental state interpretation of the user and produce reliable results.