Yuheng Feng , Qingze Liu , Aiping Liu , Ruobing Qian , Xun Chen
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引用次数: 4
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.