Prior artifact information based automatic artifact removal from EEG data

Abstract

Electroencephalogram (EEG) is susceptible to various non-neural physiological artifacts. Automatic artifact removal from EEG remains a great challenge for extracting relevant information from brain activities. In order to adapt to variable subjects and EEG acquisition environments, this paper presents a novel automatic artifact removal method based on prior artifact information. First, the wavelet-ICA algorithm, which combines of discrete wavelet transform (DWT) and independent component analysis (ICA), is utilized to separate artifact components. Then the artifact components are automatically identified using the prior artifact information, which is acquired in advance. Subsequently, signal reconstruction is performed without the identified artifact components to obtain the artifact free signals. At last, the method is validated by the improvements of the classification accuracies in a motor imagery experiment.