TY - GEN
T1 - Filter-bank local region complex-valued common spatial pattern for Motor imagery classification
AU - Park, Yongkoo
AU - Chung, Wonzoo
N1 - Funding Information:
ACKNOWLEDGMENT 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).
PY - 2020/2
Y1 - 2020/2
N2 - This paper represents a novel motor-imagery (MI) classification method based on a local region filter-bank common spatial pattern (LRFBCSP) using complexed form of electroencephalography (EEG) signals. LRFBCSP approach selects the MI-relevant local region which is constructed by individual channels and their neighbors by comparing their eigenvalue disparity. We propose an extension version of the LRFBCSP by considering the complex-valued spatial filtering rather than the real-valued spatial filtering. The complex-valued spatial filtering improves the discrimination of each local region and provides enhanced CSP features. Simulation result shows the performance improvement of the proposed method for BCI competition III dataset IVa by comparing the CSP-based methods.
AB - This paper represents a novel motor-imagery (MI) classification method based on a local region filter-bank common spatial pattern (LRFBCSP) using complexed form of electroencephalography (EEG) signals. LRFBCSP approach selects the MI-relevant local region which is constructed by individual channels and their neighbors by comparing their eigenvalue disparity. We propose an extension version of the LRFBCSP by considering the complex-valued spatial filtering rather than the real-valued spatial filtering. The complex-valued spatial filtering improves the discrimination of each local region and provides enhanced CSP features. Simulation result shows the performance improvement of the proposed method for BCI competition III dataset IVa by comparing the CSP-based methods.
KW - Bain-computer interfaces (BCIs)
KW - common spatial pattern (CSP)
KW - component
KW - electroencephalography (EEG)
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U2 - 10.1109/BCI48061.2020.9061619
DO - 10.1109/BCI48061.2020.9061619
M3 - Conference contribution
AN - SCOPUS:85084088074
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
Y2 - 26 February 2020 through 28 February 2020
ER -