Reduced burden of individual calibration process in brain-computer interface by clustering the subjects based on brain activation

Young Tak Kim, Seho Lee, Hakseung Kim, Seung Bo Lee, Seong Whan Lee, Dong Joo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2139-2143
Number of pages5
ISBN (Electronic)9781728145693
DOIs
Publication statusPublished - 2019 Oct
Event2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
Duration: 2019 Oct 62019 Oct 9

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2019-October
ISSN (Print)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
CountryItaly
CityBari
Period19/10/619/10/9

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Cite this

Kim, Y. T., Lee, S., Kim, H., Lee, S. B., Lee, S. W., & Kim, D. J. (2019). Reduced burden of individual calibration process in brain-computer interface by clustering the subjects based on brain activation. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 (pp. 2139-2143). [8914176] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2019.8914176