An empirical suggestion for collaborative learning in motor imagery-based BCIs

Eun Song Kang, Bum Chae Kim, Heung-Il Suk

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

Abstract

In modern Brain-Computer Interfaces (BCIs), it usually requires the so-called calibration session to adapt a BCI model, e.g., spatial filter and classifier, to a target subject before use, due to high intra- and inter-subject variability in brain signals. From a practical perspective, this is one of the main challenges that should be resolved, thus motivating to use information from other subjects via collaborative learning. In this study, we analyze the effects of utilizing data from other subjects and identify whether generic patterns, which are informative for general BCI, exist by conducting experiments on the BCI Competition IV-IIa dataset. Based on our two experiments of naïve inter-subject BCI and generic pattern-guided inter-subject BCI, we suggest utilizing 1) categorical information of training samples and 2) samples of generic patterns for generalization of a BCI model.

Original languageEnglish
Title of host publication4th International Winter Conference on Brain-Computer Interface, BCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467378413
DOIs
Publication statusPublished - 2016 Apr 20
Event4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of
Duration: 2016 Feb 222016 Feb 24

Publication series

Name4th International Winter Conference on Brain-Computer Interface, BCI 2016

Other

Other4th International Winter Conference on Brain-Computer Interface, BCI 2016
CountryKorea, Republic of
CityGangwon Province
Period16/2/2216/2/24

Fingerprint

Brain computer interface
Information use
Brain
Classifiers
Experiments
Calibration

Keywords

  • Brain-Computer Interface (BCI)
  • Calibration
  • Collaborative Filtering
  • Common Spatial Pattern
  • Zero-Training

ASJC Scopus subject areas

  • Human-Computer Interaction

Cite this

Kang, E. S., Kim, B. C., & Suk, H-I. (2016). An empirical suggestion for collaborative learning in motor imagery-based BCIs. In 4th International Winter Conference on Brain-Computer Interface, BCI 2016 [7457450] (4th International Winter Conference on Brain-Computer Interface, BCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2016.7457450

An empirical suggestion for collaborative learning in motor imagery-based BCIs. / Kang, Eun Song; Kim, Bum Chae; Suk, Heung-Il.

4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7457450 (4th International Winter Conference on Brain-Computer Interface, BCI 2016).

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

Kang, ES, Kim, BC & Suk, H-I 2016, An empirical suggestion for collaborative learning in motor imagery-based BCIs. in 4th International Winter Conference on Brain-Computer Interface, BCI 2016., 7457450, 4th International Winter Conference on Brain-Computer Interface, BCI 2016, Institute of Electrical and Electronics Engineers Inc., 4th International Winter Conference on Brain-Computer Interface, BCI 2016, Gangwon Province, Korea, Republic of, 16/2/22. https://doi.org/10.1109/IWW-BCI.2016.7457450
Kang ES, Kim BC, Suk H-I. An empirical suggestion for collaborative learning in motor imagery-based BCIs. In 4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7457450. (4th International Winter Conference on Brain-Computer Interface, BCI 2016). https://doi.org/10.1109/IWW-BCI.2016.7457450
Kang, Eun Song ; Kim, Bum Chae ; Suk, Heung-Il. / An empirical suggestion for collaborative learning in motor imagery-based BCIs. 4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. (4th International Winter Conference on Brain-Computer Interface, BCI 2016).
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