P300 Brain Waves Instigated Semi Supervised Video Surveillance for Inclusive Security Systems

Abstract

Soldier patrolling is a risky task at the cross borders which leads to loss of life. To overcome such risks, many researchers working on reduction of human effort using Cognitive Science through Brain Computer Interface (BCI) application. Human brain is a complex organ of body and researchers aim to build a direct communication of human brain with computer system including the Artificial Intelligence (AI) and Computational Intelligence (CI). In order to achieve such objectives, a proper brain signal capturing mechanism to be used. The appropriate signals are captured using Electroencephalogram (EEG) cap which is used to record electrical activity of brain and classified to filter P300 brain wave which is an Event Related Potential (ERP) to detect abnormal events like crawling under the Line of Control (LoC) or any illegal cross border movements of goods, drugs supply, arms supply and cargos. Brain signal is contaminated with artifacts and noises. Further work is carried on improving the Signal to Noise Ratio (SNR) quality by using appropriate filtration algorithm. The proposed filter is to use sliding Hierarchal Discriminant Classification Algorithm (sHDCA) for P300 signal to detect and classify between the target and non target component based on a multi Rapid Serial Visual Presentation (RSVP) using real time video frames from the region. As a result, it reduces the false alarm and creating the threat signature library from the filtered and classified brain signals for Comprehensive Integrated Border Management System (CIBMS).