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DOI: 10.3724/SP.J.2096-5796.21.00008

A novel SSA-CCA framework for muscle artifact removal from ambulatory EEG

Abstract

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.

Cite this article

Yuheng FENG, Qingze LIU, Aiping LIU, Ruobing QIAN, Xun CHEN. A novel SSA-CCA framework for muscle artifact removal from ambulatory EEG. Virtual Reality & Intelligent Hardware DOI:10.3724/SP.J.2096-5796.21.00008