Abstract:
Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific time period, it requires a suitable signal processing application for interpretation. Moreover, multiple-steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the P300 brainwave identification methods based on P300 evoked potential peak signal. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channels classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.
Mae Fah Luang University. Learning Resources and Educational Media Center