Independent Component Analysis (ICA) IS A MULTIVARIABLE STATISTICAL Analysis METHOD WHICH CAN BE APPLIED FOR FACE RECOGNITION PROBLEM. THE AIM OF RECOGNITION IS TO APPROXIMATELY ESTIMATE THE ComponentS FROM THE RAW IMAGE. ComponentS PLAYS AN IMPORTANT ROLE IN FACE RECOGNITION SYSTEMS. CONSEQUENTLY, THESE ComponentS ARE USED FOR EXTRACTION OF FACE IMAGE FEATURES. HOWEVER, THESE FEATURES MAY NOT BE APPROPRIATE FOR CLASSIFICATION, SINCE THE ICA METHOD DOES NOT CONSIDER THE CLASS INFORMATION. FOR THE PURPOSE OF OPTIMIZING THE PERFORMANCE OF ICA, THE DISCRIMINANT ICA (DICA) METHOD, WHICH IS A COMBINATION OF ICA AND LDA METHODS, IS UTILIZED FOR FACE RECOGNITION IN THIS STUDY. WE HAVE ALSO PROPOSED PARTICLE SWARM OPTIMIZATION METHOD TO IMPROVE THE DICA PERFORMANCE, IN WHICH PSO IS USED INSTEAD OF THE GRADIENT APPROACH FOR LEARNING DICA. THE RESULTS OF PSO-DICA METHOD CONFIRM OUR IDEA IN CLASSIFICATION EXPERIMENTS COMPARED TO OTHER METHODS. USING PROPOSED METHOD ON YALE B DATASET, GIVES AN AVERAGE CLASSIFICATION ACCURACY OF 92.169% COMPARED WITH AN ACCURACY OF 91.322% USING WHEN DICA AND ACCURACY OF 89.77% COMPARED WITH ICA AND ACCURACY OF 86.18% USING PCA AND ALSO ACCURACY OF 84.76% USING LDA

THIS ARTICLE PRESENTS THE USE OF Independent Component Analysis (ICA) TO CHARACTERIZE THE CHANGES OF EEG SIGNALS DURING IMAGINING HAND CLOSING, HAND OPENING, AND DURING A RESTING STATE. ICA IS A USEFUL EXTENSION OF THE PRINCIPLE Component Analysis (PCA) THAT ALLOWS BLIND SEPARATION OF SOURCES, LINEARLY MIXED, ASSUMING ONLY THE STATISTICAL INDEPENDENCE OF THESE SOURCES. THIS SUGGESTS THAT ICA CAN SEPARATE DIFFERENT Independent BRAIN ACTIVITIES DURING MOTOR IMAGERY INTO SEPARATE ComponentS. FOR THIS PURPOSE, DIFFERENT EXPERIMENTS WERE CONDUCTED ON NORMAL SUBJECTS. THE EEG SIGNALS WERE RECORDED AT A SAMPLING RATE OF 256 BY SCALP ELECTRODES PLACED ACCORDING TO THE INTERNATIONAL 10-20 SYSTEM. ANOTHER CHANNEL WAS ADDED TO RECORD EYE BLINKS BY PLACING AN ELECTRODE ON THE FOREHEAD ABOVE THE LEFT BROW LINE. THE RESULTS SHOW THAT THE VISUAL STIMULUS RESPONSE, P300 Component, MOTOR IMAGERY POTENTIAL, AND BACKGROUND EEG ComponentS CAN BE SEPARATED BY THE Independent Component Analysis OF EEG SIGNALS DURING HAND MOVEMENT IMAGINATION. TIME FREQUENCY Analysis OF THE Independent Component OF THE EEG SIGNAL SHOWS THAT THE POWER OF ALPHA BAND IS DECREASED DURING MOTOR IMAGERY. THE RATE OF DECREASE DURING HAND CLOSING IS MORE THAN THAT DURING OPENING. MOREOVER, THE BEGINNING OF MOTOR IMAGINATION IS ACCOMPANIED BY AN INCREASE OF DELTA AND TETA BAND POWER.