TY - GEN
T1 - An Optimal Smooth Model based EEG Analysis Method
AU - Chen, Chao
AU - Shang, Tianxu
AU - Belkacem, Abdelkader Nasreddine
AU - Zhang, Shanting
AU - Lu, Lin
AU - Li, Penghai
N1 - Publisher Copyright:
© 2021 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2021/7/26
Y1 - 2021/7/26
N2 - Aiming at the problem of unsatisfactory noise removal effect during the preprocessing of MI EEG signals, an optimal smooth model based on empirical mode decomposition is introduced. First, use empirical mode decomposition (EMD) to decompose the complex signal into a certain number of intrinsic mode functions (IMFs) and a remainder function (r), and then construct a smooth model function based on the mean square error and smoothing index, using different The function of the intrinsic mode is combined with the optimal smooth model function to reduce noise and obtain the optimal solution. The research results show that compared with several denoising methods commonly used in other literature, this method has improved SNR, RMSE, and PE three evaluation indicators. The proposed method can provide a reference for the preprocessing of motor imagery signals.
AB - Aiming at the problem of unsatisfactory noise removal effect during the preprocessing of MI EEG signals, an optimal smooth model based on empirical mode decomposition is introduced. First, use empirical mode decomposition (EMD) to decompose the complex signal into a certain number of intrinsic mode functions (IMFs) and a remainder function (r), and then construct a smooth model function based on the mean square error and smoothing index, using different The function of the intrinsic mode is combined with the optimal smooth model function to reduce noise and obtain the optimal solution. The research results show that compared with several denoising methods commonly used in other literature, this method has improved SNR, RMSE, and PE three evaluation indicators. The proposed method can provide a reference for the preprocessing of motor imagery signals.
KW - EEG signal denoising
KW - Electroencephalogram(EEG)
KW - Motor Imagery
UR - http://www.scopus.com/inward/record.url?scp=85117307397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117307397&partnerID=8YFLogxK
U2 - 10.23919/CCC52363.2021.9550356
DO - 10.23919/CCC52363.2021.9550356
M3 - Conference contribution
AN - SCOPUS:85117307397
T3 - Chinese Control Conference, CCC
SP - 3299
EP - 3304
BT - Proceedings of the 40th Chinese Control Conference, CCC 2021
A2 - Peng, Chen
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 40th Chinese Control Conference, CCC 2021
Y2 - 26 July 2021 through 28 July 2021
ER -