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  Predicting BCI subject performance using probabilistic spatio-temporal filters

Suk, H.-I., Fazli, S., Mehnert, J., Müller, K.-R., & Lee, S.-W. (2014). Predicting BCI subject performance using probabilistic spatio-temporal filters. PLoS One, 9(2): e87056. doi:10.1371/journal.pone.0087056.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0015-3A15-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-81D8-7
Genre: Journal Article

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© 2014 Suk et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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 Creators:
Suk, Heung-Il1, Author
Fazli, Siamac1, Author
Mehnert, Jan2, 3, Author              
Müller, Klaus-Robert1, 2, Author
Lee, Seong-Whan1, Author
Zhan, Wang1, Contributor
Affiliations:
1Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, ou_persistent22              
2Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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 Abstract: Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian–and thereby probabilistic–framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms–a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects’ performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject ‘prototypes’ (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

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Language(s): eng - English
 Dates: 2013-11-062013-12-172014-02-14
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pone.0087056
PMID: 24551050
PMC: PMC3925079
Other: eCollection 2014
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 9 (2) Sequence Number: e87056 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: /journals/resource/1000000000277850