TY - GEN
T1 - Lateralization of alpha oscillation under preparation Lead to Efficiency of Motor Imagery
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
AU - Lee, Seho
AU - Lee, Choel Hui
AU - Kim, Hakseung
AU - Kim, Dong Joo
N1 - Funding Information:
* This research was supported in part by Institute for Information & communications Technology Promotion(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), in part by Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science
Funding Information:
and ICT, MSIT) under Grant 2019R1A2C1003399, and in part by Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, MSIT) under Grant NRF- 2020R1C1C1006773.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - Electroencephalography (EEG) is a primary modality for estimating user intention in the brain-computer interface (BCI). In particular, BCI has been widely used to detect the intention of users in motor imagery (MI)-based tasks. Although the MI classification accuracy has been largely enhanced from previous efforts, MI-BCI studies have focused on extracting features only during MI tasks, not during the preparatory phases. The increment of alpha band power is induced by performing a task with attention. This study proposes a n approach for increasing MI-BCI performance by analyzing brain state in preparatory before the task. EEG recordings of nine healthy subjects from the open BCI dataset were investigated. The alpha lateralization index (ALI) was calculated for each trial and high ALI trials were utilized for learning lateralization-based model. MI classification accuracy using the lateralization-based model marked high performance (median accuracy = 63.2 %; interquartile range (IQR) = 50.0% - 54.8%) than the total trial-based approach (median accuracy = 52.0%; IQR = 50.0 % - 54.8%) with statistical significance (p = 0.018). This study suggests alpha lateralization which is an imbalance pattern between ipsilateral and contralateral is one of the main factors for improvement of performance. Accordingly, since the alpha liberalization before MI task could exert an effect on the MI phase, the analysis combined preparation with MI would derive highly benefit for the MI classification.
AB - Electroencephalography (EEG) is a primary modality for estimating user intention in the brain-computer interface (BCI). In particular, BCI has been widely used to detect the intention of users in motor imagery (MI)-based tasks. Although the MI classification accuracy has been largely enhanced from previous efforts, MI-BCI studies have focused on extracting features only during MI tasks, not during the preparatory phases. The increment of alpha band power is induced by performing a task with attention. This study proposes a n approach for increasing MI-BCI performance by analyzing brain state in preparatory before the task. EEG recordings of nine healthy subjects from the open BCI dataset were investigated. The alpha lateralization index (ALI) was calculated for each trial and high ALI trials were utilized for learning lateralization-based model. MI classification accuracy using the lateralization-based model marked high performance (median accuracy = 63.2 %; interquartile range (IQR) = 50.0% - 54.8%) than the total trial-based approach (median accuracy = 52.0%; IQR = 50.0 % - 54.8%) with statistical significance (p = 0.018). This study suggests alpha lateralization which is an imbalance pattern between ipsilateral and contralateral is one of the main factors for improvement of performance. Accordingly, since the alpha liberalization before MI task could exert an effect on the MI phase, the analysis combined preparation with MI would derive highly benefit for the MI classification.
UR - http://www.scopus.com/inward/record.url?scp=85098881789&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9283439
DO - 10.1109/SMC42975.2020.9283439
M3 - Conference contribution
AN - SCOPUS:85098881789
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2502
EP - 2505
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 October 2020 through 14 October 2020
ER -