Motor Activity Brain Signals Recognition in Seaman by using Blind Source Separation Algorithm

A Brain-Computer Interface (BCI) is a system that enables communication and control without using brain normal output pathways to peripheral nerves and muscles. A BCI directly measures brain activity associated with the user’s intent and translates the recorded brain activity into suitable control signals using signal processing and pattern recognition methods. Motor imagination of different body parts is one of the activities used in BCI. The system can understand the user's intention when it can distinguish him/her from different motor imaginations. Signal processing and pattern recognition and partitioning consist of preprocessing and signal quality enhancement, feature extraction, and classification. Spatial filtering has great importance in signal quality enhancement since it can reduce artifacts and irrelevant brain activities. Surface Laplacian filtering is one popular technique for spatial filtering. This paper is devoted to concentrating the processing part on brain sources related to motor imagination by blind source separation. Independent component analysis is employed for blind source separation, and implementations on datasets show the efficiency of the proposed method.