Key result
A real-time artifact removal algorithm using canonical correlation analysis and Gaussian mixture clustering effectively removed artifacts from EEG recordings while preserving important characteristics.
A novel real-time algorithm using CCA and GMM effectively removes noncerebral artifacts from EEG signals, potentially improving cognitive research data quality.
May enhance EEG signal fidelity in research settings; leaves open prospective validation before clinical adoption.
Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
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Lin et al. (2018) studied EEG artifacts. Real-time artifact removal algorithm based on CCA, feature extraction, and GMM was evaluated on Artifact removal from EEG recordings. A real-time artifact removal algorithm using canonical correlation analysis and Gaussian mixture clustering effectively removed artifacts from EEG recordings while preserving important characteristics.
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