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 ERP 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.

3D Reconstruction and Volume Estimation of Jujube Using Consumer-Grade RGB-Depth Sensor

“…Outlier points are filtered by the radius contour line outlier removal filter [24]. There are discrete points around the collected point cloud, also called outliers, in addition, StatisticalOutlierRemoval filtering ( SORF) method [25], which uses statistical methods to remove outliers. The maximum radius of the set retrieval is 5 mm, and the number of points is less than 10 will be judged as cluster points.…”