1. Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
- Author
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Han, Siqi, Zhang, Chao, Lei, Jiaxin, Han, Qingquan, Du, Yuhui, Wang, Anhe, Bai, Shuo, and Zhang, Milin
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the artifact detection task and a 82.79% accuracy on the 6-category eye movement classification task. A statistical value-based artifact removal method is proposed and evaluated on a public EEG database, where an accuracy improvement of 3.46% is obtained on the 3-category emotion classification task. In order to make a confident decision of each 5s EEG segment, the algorithm requires only 0.66M multiplication operations. Compared to the state-of-the-art approaches in artifact detection and removal, the proposed method features higher detection accuracy and lower computational cost, which makes it a more suitable solution to be integrated into a real-time and artifact robust Brain-Machine Interface (BMI)., Comment: 5 pages, 4 figures, published by TCAS-II
- Published
- 2024
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