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 electri...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 few channel 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-the art 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.展开更多
基金Supported by the National Natural Science Foundation of China(61922075)the USTC Research Funds of the Double First-Class Initiative(YD2100002004).
文摘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 few channel 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-the art 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.
