earticle

영어

In this study a noble TMS-induced artifact removal method is developed and discussed by estimating its parameters for various aspects of data, such as sampling rate, filtering order and ICA decomposition method, in both the EEG time series and in the independent components of the EEG by using the EEG data obtained from four healthy subjects who were receiving single pulse TMS-EEG and sham-EEG stimulus on the left Broca’s area. A total of four healthy male subjects without any neurological disorder were selected in this study. ICA filters trained on the reduced version of 60 channel EEG data collected during single pulse TMS-EEG and sham-EEG recordings and identified the reduced number of statistically independent source channels. The decomposition algorithm of ICA considered in this study includes Jader , FastICA and cICA. The ICA components originating from the TMS-induced artifact are classified by comparing the cross-correlation coefficients between single pulse TMS-EEG and sham-EEG stimulus after ICA decomposition. Then, the estimation of parameters in the TMS-induced artifact removal for sampling rate 1.45kHz, filtering order 100 and ICA decomposition method FastICA was evaluated by the change of the ratio of the cross-correlation coefficients between single pulse TMS-EEG and sham-EEG stimulus before and after the ICA decomposition. The results showed the consistency in the assessment of the availability of the TMS-induced artifact removal suggesting the efficiency and the reliability of the method developed in this study.