Key result
An outlier detection-based method using one-class support vector machine outperformed threshold-based methods for removing various EEG artifacts without excessive suppression of brain activities.
Why the study?
Existing threshold-based artifact rejection methods for few-channel EEGs lack effectiveness for specific artifacts and excessively suppress brain activities.
The proposed outlier detection-based method provides a fully data-driven and adaptive approach for removing various EEG artifacts in a single process without excessive suppression of brain activities.
Answers are grounded in this paper and cite their sources.
Offers a data-driven approach to preserve true EEG signals during artifact removal; leaves open whether this adaptive.
Objective. The electroencephalogram (EEG) is one of the most important brain-imaging tools. The few-channel EEG is more suitable and affordable for practical use as a wearable device. Removing artifacts from collected EEGs is a prerequisite for accurately interpreting brain function and state. Previous studies proposed methods combining signal decomposition with the blind source separation (BSS) algorithms, but most of them used threshold-based criteria for artifact rejection, resulting in a lack of effectiveness in removing specific artifacts and the excessive suppression of brain activities. In this study, we proposed an outlier detection-based method for artifact removal under the few-channel condition. Approach . The underlying components (sources) were extracted using the decomposition-BSS schema. Based on our assumptions that in the feature space, the artifact-related components are dispersed, while the components related to brain activities are closely distributed, the artifact-related components were identified and rejected using one-class support vector machine. The assumptions were validated by visualizing the distribution of clusters of components. Main results . In quantitative analyses with semisimulated data, the proposed method outperformed the threshold-based methods for various artifacts, including muscle artifact, ocular artifact, and power line noise. With a real dataset and an event-related potential dataset, the proposed method demonstrated good performance in real-life situations. Significance . This study provided a fully data-driven and adaptive method for removing various artifacts in a single process without excessive suppression of brain activities.
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Chen et al. (2022) studied EEG artifacts. Outlier detection-based method using one-class support vector machine vs. Threshold-based methods was evaluated on Artifact removal performance. An outlier detection-based method using one-class support vector machine outperformed threshold-based methods for removing various EEG artifacts without excessive suppression of brain activities.
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