An outlier detection-based method for artifact removal of few-channel EEGs

Abstract

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.