Multiple Artifact Detection Based on Adaptive Scalp Region Selection and Classifier Fusion

Long-term electroencephalography (EEG) rec- ordings contain multiple types of artifacts (eye blink artifacts, chewing artifacts, electromyography (EMG) artifacts, etc.), that severely interfere with the EEG reading by clinicians and researchers. Existing research often focuses on specific artifact detection, which cannot handle the complex multitype artifact detection. Therefore, a multiple artifact detection method based on adaptive scalp region selection and classifier fusion is proposed in this article. The main contributions of the article are as follows: 1) according to the characteristics of different scalp area generated by artifacts, adaptive brain region screening is realized. 2) ReliefF and mRMR are fused to realize two-stage feature evaluation and reduce the feature redundancy. 3) Multimodel cascade fusion can bring stronger robustness and better generalization. The data used in the study are collected from real patients suffering from benign epilepsy with centro-temporal spikes (BECT) at the Children’s Hospital of Zhejiang University School of Medicine (CHZU). Compared with the state-of-the-art methods, the proposed method shows the best performance in the detection of multitype artifacts, and achieves the F1-score of 87%, precision of 87%, recall of 88%, and accuracy of 93% on the CHZU dataset.