Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911837400858624 |
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| author | Han, Siqi Zhang, Chao Lei, Jiaxin Han, Qingquan Du, Yuhui Wang, Anhe Bai, Shuo Zhang, Milin |
| author_facet | Han, Siqi Zhang, Chao Lei, Jiaxin Han, Qingquan Du, Yuhui Wang, Anhe Bai, Shuo Zhang, Milin |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08199 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG Han, Siqi Zhang, Chao Lei, Jiaxin Han, Qingquan Du, Yuhui Wang, Anhe Bai, Shuo Zhang, Milin Signal Processing 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). |
| title | Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2404.08199 |