TY - GEN
T1 - Reduced burden of individual calibration process in brain-computer interface by clustering the subjects based on brain activation
AU - Kim, Young Tak
AU - Lee, Seho
AU - Kim, Hakseung
AU - Lee, Seung Bo
AU - Lee, Seong Whan
AU - Kim, Dong Joo
N1 - Funding Information:
*This work was supported 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).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Electroencephalography (EEG) is the primary modality for estimating the user intention in brain-computer interface (BCI). However, the suppression of the inter-subject variability (ISV) remains as a major challenge in constructing a reliable EEG-based BCI model. Subject-specific classification models have been widely used to avoid ISV, however these inherently involve time-consuming individual calibration process. This study speculated that the calibration could be minimized via clustering BCI subjects into subgroups by their respective similarity in brain power distribution at the resting state and conducted a proof-of-concept investigation. EEG recordings of twenty-nine healthy subjects from open motor imagery (MI) dataset were used in this study. K-means clustering based on brain activation in alpha, low beta and high beta-band at resting state divided the subjects into three subgroups. The efficacy of band-clustering was evaluated by comparing its MI classification performance (left-or right-hand gripping) to subject-specific and general models. Among the subjects in a cluster, ISV was lower than that in twenty-nine subjects, especially in the alpha-band. The MI classification accuracy using the cluster-specific model on the alpha-band was marked high performance (median accuracy 68.8%). The cluster-specific model had significantly high accuracy compared to general model (median accuracy = 64.6%). Furthermore, the difference of MI classification accuracy between the cluster-specific model on the alpha band and subject-specific model is not significant (median accuracy = 69.3%). Consequently, establishing a model by grouping clusters using similar brain activation patterns was highly beneficial for the MI classification without individual calibration process.
AB - Electroencephalography (EEG) is the primary modality for estimating the user intention in brain-computer interface (BCI). However, the suppression of the inter-subject variability (ISV) remains as a major challenge in constructing a reliable EEG-based BCI model. Subject-specific classification models have been widely used to avoid ISV, however these inherently involve time-consuming individual calibration process. This study speculated that the calibration could be minimized via clustering BCI subjects into subgroups by their respective similarity in brain power distribution at the resting state and conducted a proof-of-concept investigation. EEG recordings of twenty-nine healthy subjects from open motor imagery (MI) dataset were used in this study. K-means clustering based on brain activation in alpha, low beta and high beta-band at resting state divided the subjects into three subgroups. The efficacy of band-clustering was evaluated by comparing its MI classification performance (left-or right-hand gripping) to subject-specific and general models. Among the subjects in a cluster, ISV was lower than that in twenty-nine subjects, especially in the alpha-band. The MI classification accuracy using the cluster-specific model on the alpha-band was marked high performance (median accuracy 68.8%). The cluster-specific model had significantly high accuracy compared to general model (median accuracy = 64.6%). Furthermore, the difference of MI classification accuracy between the cluster-specific model on the alpha band and subject-specific model is not significant (median accuracy = 69.3%). Consequently, establishing a model by grouping clusters using similar brain activation patterns was highly beneficial for the MI classification without individual calibration process.
UR - http://www.scopus.com/inward/record.url?scp=85076749692&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8914176
DO - 10.1109/SMC.2019.8914176
M3 - Conference contribution
AN - SCOPUS:85076749692
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2139
EP - 2143
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -