A Novel SSA-CCA Framework forMuscle Artifact Removal from Ambulatory EEG

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. 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. 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. This promising approach, based on its effectiveness and low time cost, is suitable for real-world biomedical signal processing applications.