تشخيص و بررسي كاهش دامنه P300 در پتانسيل هاي وابسته به رويداد شنوايي تك ثبت با استفاده از الگوريتم ژنتيك و طبقه بندي كننده شبكه عصبي

Single Trial P300 Recognition in Auditory Event Related Potential Using Genetic Algorithm and Neural Network Classifier

P300 is known as the most prominent component between cognitive components in electrical brain activity. According to done research, when brain encounters an inconsistent stimulation during processing a series of usual stimulation, a P300 component appears in recorded brain signal which could distinguishes from usual ones. Amplitude of P300 decreases after a short during act of auditory simulation; so that we face difficulty in recognition of component features. In the research we considered reduction of the amplitude of P300 with five auditory stimulations and its reasons in three separate record blocks as well as recognition of the component with Neural Network and Genetic Algorithm. Finally single-trial recordings containing P300 component from single-trial recordings without P300 component have been discriminated by six optimum features as Neural Network classifier input in Pz channel with accuracy of 80.55% in learning data and 50% in test data in the first block.