Automatic Elimination of High Amplitude Artifacts in EEG Signals

摘要

High amplitude artifacts represent a problem during EEG recordings in neuroscience research. Taking this into account, this paper proposes a method to identify high amplitude artifacts with no requirement for visual inspection, electrooculogram (EOG) reference channel or user assigned parameters. A potential solution to the high amplitude artifacts (HAA) elimination is presented based on the blind source separation technique. The assumption underlying the selection of components is that HAA are independent of the EEG signal and different HAA can be generated during the EEG recordings. Therefore, the number of components related to HAA is variable and depends on the processed signal, which means that the method is adaptable to the input signal. The results demonstrate that the proposed method preferably removes the signal associated to the delta band and maintains the EEG signal information in other bands with a high relative precision, thus improving the quality of the EEG signal. A case study with EEG signals obtained during performance on the Halstead Category Test (HCT) is presented. After HAA removal, data analysis revealed an error-related frontal ERP wave: the feedback-related negativity (FRN) in response to feedback stimuli.