Abstract
Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.
Highlights
As a biological signal that reflects potential changes in complex brain activities, electroencephalogram (EEG) plays an important role in human brain research, disease diagnosis, brain-computer interfaces (BCI), and so on
We investigated the validity of our method via correlation analysis for identifying artifact components
We plotted the corresponding time-domain components of the waveletindependent components (WICs) to visually inspect and identify the artifacts and compared the results of automatic identification by correlation analysis
Summary
As a biological signal that reflects potential changes in complex brain activities, electroencephalogram (EEG) plays an important role in human brain research, disease diagnosis, brain-computer interfaces (BCI), and so on. The most severe artifacts include eye movement (electrooculography, EOG) and muscle movement (electromyography, EMG) artifacts [1] These undesired signals can complicate EEG data or can be misread as the physiological phenomena of interest. Artifact avoidance and artifact rejection were used to handle artifacts in early studies These approaches might not acquire sufficient valid data from actual experiments, in which eye blinking, swallowing, or other nonneural physiological activities are inevitable [2]. Linear regression [4, 5] assumes that EEG measurement is a linear combination of real EEG and artifacts and they are not related This straightforward technique works well for EOG artifacts with a reference channel, but the assumption is inadequate for removing EMG artifacts. A more extensive review of artifact reduction techniques can be obtained from the literature [1]
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