TY - GEN
T1 - Phase Transition in previous Motor Imagery affects Efficiency of Motor Imagery based Brain-computer Interface
AU - Jung, Min Kyung
AU - Lee, Seho
AU - Wang, In Nea
AU - Song, Ha Yoon
AU - Kim, Hakseung
AU - Kim, Dong Joo
N1 - Funding Information:
This research was supported by the 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) and the National Research Foundation of Korea (NRF) grant(2019R1A2C1003399, 2020R1C1C1006773). *Asterisk denotes the corresponding author.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/2/22
Y1 - 2021/2/22
N2 - A electroencephalography (EEG) based braincomputer interface (BCI) provides a communication channel to operate the external environment by decoding brain patterns. Movement-Related Cortical Potentials (MRCPs) are one particular type of EEG pattern during movement of peripheral limbs. The performance of motor imagery (MI) is related to pattern of MRCPs by planning simulation. In resent decade, MI-based BCI have shown potential as its performance significantly improved. In this study, the feasibility of selected-based method was proposed by compared with conventional method. The detection accuracy overall performances were 72.42± 3.12\%. When a top 97.8% trial is selected, overall performance improved approximately 3.15% compared to baseline. When MI were analyzed in non-selected trials, C3 and C4 channel showed no different aspects in left and right class respectively. The brain state was changed after the cue appeared, and these power of delta band appeared in all subjects. The performance of classification was improved by rejecting trials with no difference between the state before and after cue.
AB - A electroencephalography (EEG) based braincomputer interface (BCI) provides a communication channel to operate the external environment by decoding brain patterns. Movement-Related Cortical Potentials (MRCPs) are one particular type of EEG pattern during movement of peripheral limbs. The performance of motor imagery (MI) is related to pattern of MRCPs by planning simulation. In resent decade, MI-based BCI have shown potential as its performance significantly improved. In this study, the feasibility of selected-based method was proposed by compared with conventional method. The detection accuracy overall performances were 72.42± 3.12\%. When a top 97.8% trial is selected, overall performance improved approximately 3.15% compared to baseline. When MI were analyzed in non-selected trials, C3 and C4 channel showed no different aspects in left and right class respectively. The brain state was changed after the cue appeared, and these power of delta band appeared in all subjects. The performance of classification was improved by rejecting trials with no difference between the state before and after cue.
KW - brain-computer interface
KW - electroencephalography
KW - motor imagery
KW - movement-related cortical potentials
UR - http://www.scopus.com/inward/record.url?scp=85104894159&partnerID=8YFLogxK
U2 - 10.1109/BCI51272.2021.9385321
DO - 10.1109/BCI51272.2021.9385321
M3 - Conference contribution
AN - SCOPUS:85104894159
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 -