TY - GEN
T1 - EEG feature selection using orthogonal regression
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
AU - Xu, Xueyuan
AU - Wei, Fulin
AU - Zhu, Zhiyuan
AU - Liu, Jianhong
AU - Wu, Xia
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020/5
Y1 - 2020/5
N2 - A common drawback of the EEG applications is that the volume conduction of human head leads to lots of redundant information in EEG recordings. To reduce the redundancy and choose informative EEG features, in this paper, we propose an EEG feature selection technique, termed as Feature Selection with Orthogonal Regression (FSOR). Compared with classical feature selection methods, for nonlinear and non-stationary EEG signals, FSOR can employ orthogonal regression to preserve more discriminative information in the subspace. To verify the EEG feature selection performance, we collected a multichannel EEG dataset for emotion recognition and compared FSOR with two popular feature selection methods. The experimental results demonstrate the advantage of FSOR method over others for reducing the redundant information among the EEG relevant features. Additionally, we found that the absolute power ratio of beta wave to theta wave is the most discriminative feature, and beta band is the critical band for emotion recognition.
AB - A common drawback of the EEG applications is that the volume conduction of human head leads to lots of redundant information in EEG recordings. To reduce the redundancy and choose informative EEG features, in this paper, we propose an EEG feature selection technique, termed as Feature Selection with Orthogonal Regression (FSOR). Compared with classical feature selection methods, for nonlinear and non-stationary EEG signals, FSOR can employ orthogonal regression to preserve more discriminative information in the subspace. To verify the EEG feature selection performance, we collected a multichannel EEG dataset for emotion recognition and compared FSOR with two popular feature selection methods. The experimental results demonstrate the advantage of FSOR method over others for reducing the redundant information among the EEG relevant features. Additionally, we found that the absolute power ratio of beta wave to theta wave is the most discriminative feature, and beta band is the critical band for emotion recognition.
KW - Discriminative feature
KW - EEG feature selection
KW - Embedded approaches
KW - Orthogonal regression
KW - Redundant information
UR - http://www.scopus.com/inward/record.url?scp=85091154035&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9054457
DO - 10.1109/ICASSP40776.2020.9054457
M3 - Conference contribution
AN - SCOPUS:85091154035
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1239
EP - 1243
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 May 2020 through 8 May 2020
ER -