Abstract
Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Highlights
Quantitative analysis and interpretation of human electroencephalographic (EEG) signals can benefit various applications, such as the study of human brain functional states, to evaluate drug effects, to diagnose psychiatric and neurological disorders, to use brain-controlled devices to assist disabled people through Brain-Computer Interfaces (BCIs) and so on.EEG signals are recorded from the scalp surface around the head with electrodes, which, may be contaminated by interferences
One is that the EEG data set was recorded with limited number of channels, implying that the blind source separation (BSS) algorithms can only separate the signal into six or even less underlying sources
On such a data set, both second-order blind identification (SOBI) and independent component analysis (ICA) spread the artifacts in several components due to their drawbacks, considerable neural information got lost by rejecting more than necessary number of artifactual components during the reconstruction
Summary
Quantitative analysis and interpretation of human electroencephalographic (EEG) signals can benefit various applications, such as the study of human brain functional states, to evaluate drug effects, to diagnose psychiatric and neurological disorders, to use brain-controlled devices to assist disabled people through Brain-Computer Interfaces (BCIs) and so on. The classic BSS techniques such as independent component analysis (ICA), second-order blind identification (SOBI), may not be effective on the highly non-stationary EOG artifact-contaminated EEG recordings. EMD usually demonstrates superior performance on non-stationary data to other methods [34,35,36,37], such as Fourier or the wavelet transforms, in which the basis needs to be manually specified Experiments on both artificially contaminated data and publicly available real EEG recordings have been conducted, and the results show that the proposed method can effectively improve the artifact correction on raw EEG recordings
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