A Novel SSA-CCA Framework forMuscle Artifact Removal from Ambulatory EEG
| Autor: | Yuheng Feng, Qingze Liu, Aiping Liu, Ruobing Qian, Xun Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Virtual Reality & Intelligent Hardware, Vol 4, Iss 1, Pp 1-21 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2096-5796 43558739 |
| DOI: | 10.1016/j.vrih.2022.01.001 |
| Popis: | Background: Electroencephalography (EEG) has gained popularity in various types of biomedical applications as a signal source that can be easily acquired and conveniently analyzed. However, owing to a complex scalp electrical environment, EEG is often polluted by diverse artifacts, with electromyography artifacts being the most difficult to remove. In particular, for ambulatory EEG devices with a restricted number of channels, dealing with muscle artifacts is a challenge. Methods: In this study, we propose a simple but effective novel scheme that combines singular spectrum analysis (SSA) and canonical correlation analysis (CCA) algorithms for single-channel problems and then extend it to a fewchannel case by adding additional combining and dividing operations to channels. Results: We evaluated our proposed framework on both semi-simulated and real-life data and compared it with some state-of-theart methods. The results demonstrate this novel framework's superior performance in both single-channel and few-channel cases. Conclusions: This promising approach, based on its effectiveness and low time cost, is suitable for real-world biomedical signal processing applications. |
| Databáze: | Directory of Open Access Journals |
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