TY - GEN
T1 - Motor Imagery Classification using local region CSP features with high-gamma band
AU - Lee, Jinwoo
AU - Chung, Wonzoo
N1 - Funding Information:
This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user’s thought via AR/VR interface) and Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/2/22
Y1 - 2021/2/22
N2 - In this paper, we present enhanced MI classification method in electroencephalogram(EEG)-based Brain-Computer Interfaces(BCI) with high-gamma band and local region CSP features. In order to improve the performance of MI classification, the use of the local region CSP feature with high eigenvalue disparity score and the high-gamma band related to advanced cognitive information processing such as reasoning and judgment can provide improved performance compared to existing CSP based methods. As a result of the experiment through BCI competition III-IVa dataset, it is shown that MI classification performance is improved through the proposed method.
AB - In this paper, we present enhanced MI classification method in electroencephalogram(EEG)-based Brain-Computer Interfaces(BCI) with high-gamma band and local region CSP features. In order to improve the performance of MI classification, the use of the local region CSP feature with high eigenvalue disparity score and the high-gamma band related to advanced cognitive information processing such as reasoning and judgment can provide improved performance compared to existing CSP based methods. As a result of the experiment through BCI competition III-IVa dataset, it is shown that MI classification performance is improved through the proposed method.
KW - Brain-computer interface (BCI)
KW - Common Spatial Pattern (CSP)
KW - High-gamma band
KW - Local features
KW - electroencephalography (EEG)
UR - http://www.scopus.com/inward/record.url?scp=85104896287&partnerID=8YFLogxK
U2 - 10.1109/BCI51272.2021.9385290
DO - 10.1109/BCI51272.2021.9385290
M3 - Conference contribution
AN - SCOPUS:85104896287
T3 - 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
BT - 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Y2 - 22 February 2021 through 24 February 2021
ER -