TY - GEN
T1 - An empirical suggestion for collaborative learning in motor imagery-based BCIs
AU - Kang, Eun Song
AU - Kim, Bum Chae
AU - Suk, Heung Il
N1 - Funding Information:
This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).
PY - 2016/4/20
Y1 - 2016/4/20
N2 - 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.
AB - 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.
KW - Brain-Computer Interface (BCI)
KW - Calibration
KW - Collaborative Filtering
KW - Common Spatial Pattern
KW - Zero-Training
UR - http://www.scopus.com/inward/record.url?scp=84969222331&partnerID=8YFLogxK
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U2 - 10.1109/IWW-BCI.2016.7457450
DO - 10.1109/IWW-BCI.2016.7457450
M3 - Conference contribution
AN - SCOPUS:84969222331
T3 - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
BT - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
Y2 - 22 February 2016 through 24 February 2016
ER -