TY - GEN
T1 - Self-paced training on motor imagery-based BCI for minimal calibration time
AU - Kim, Seon Min
AU - Lee, Min Ho
AU - Lee, Seong Whan
N1 - Funding Information:
This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the “SW Starlab” (IITP-2015-1107) supervised by the IITP(Institute for Information & communications Technology Promotion).
Funding Information:
ACKNOWLEDGMENT This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the “SW Starlab” (IITP-2015-1107) supervised by the IITP(Institute for Information & communications Technology Promotion).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - Motor imagery (MI)-based brain-computer interface (BCI) allows users to control external devices using the brain signal patterns induced by the imagination of movements. Since these patterns have high variability between subjects and sessions, the BCI system necessarily requires 20-30 minutes for the calibration process each time the system is used. This time-consuming process requires a high level of the user's concentration; most users experience uncomfortable feelings such as tiredness, exhaustion, and loss of attention, which are symptoms of mental fatigue. In this paper, we introduce a self-paced training that terminates the calibration process within a few minutes. In this training paradigm, users perform MI tasks continuously without an inter-stimulus-interval (ISI). Also, we propose a data selection method to extract the most prominent features from the short calibration data by assuming the data distribution probabilistically and using the prior knowledge of event-related desynchronization (ERD) patterns. The results from 19 subjects indicate that the proposed method gained a comparable classification performance to the conventional method but with a much shorter calibration period (12 min/73.8%, 30 min/76.1%, respectively). In this regard, the proposed method could be of great benefit for real-world BCI applications by providing a quicker calibration process.
AB - Motor imagery (MI)-based brain-computer interface (BCI) allows users to control external devices using the brain signal patterns induced by the imagination of movements. Since these patterns have high variability between subjects and sessions, the BCI system necessarily requires 20-30 minutes for the calibration process each time the system is used. This time-consuming process requires a high level of the user's concentration; most users experience uncomfortable feelings such as tiredness, exhaustion, and loss of attention, which are symptoms of mental fatigue. In this paper, we introduce a self-paced training that terminates the calibration process within a few minutes. In this training paradigm, users perform MI tasks continuously without an inter-stimulus-interval (ISI). Also, we propose a data selection method to extract the most prominent features from the short calibration data by assuming the data distribution probabilistically and using the prior knowledge of event-related desynchronization (ERD) patterns. The results from 19 subjects indicate that the proposed method gained a comparable classification performance to the conventional method but with a much shorter calibration period (12 min/73.8%, 30 min/76.1%, respectively). In this regard, the proposed method could be of great benefit for real-world BCI applications by providing a quicker calibration process.
KW - Brain-computer interface (BCI)
KW - Calibration time
KW - Electroencephalography (EEG)
KW - Motor imagery (MI)
KW - Self-paced training
UR - http://www.scopus.com/inward/record.url?scp=85044388731&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8122963
DO - 10.1109/SMC.2017.8122963
M3 - Conference contribution
AN - SCOPUS:85044388731
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 2297
EP - 2301
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -