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  Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

İşcan, Z., & Nikulin, V. V. (2018). Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations. PLoS One, 13(1): e0191673. doi:10.1371/journal.pone.0191673.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-3616-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A115-F
Genre: Journal Article

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 Creators:
İşcan, Zafer1, 2, 3, Author
Nikulin, Vadim V.1, 4, 5, Author              
Affiliations:
1Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              
2Institut national de la santé et de la recherche médicale, Paris, France, ou_persistent22              
3Neurospin Center, Institut national de la santé et de la recherche médicale, Paris, France, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
5Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              

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 Abstract: Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.

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Language(s): eng - English
 Dates: 2017-07-052018-01-092018-01-23
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pone.0191673
PMID: 29360843
PMC: PMC5779700
Other: eCollection 2018
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Project name : Russian Academic Excellence Project 5–100
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Funding program : -
Funding organization : Ministry of Science and Higher Education of the Russian Federation

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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: 13 (1) Sequence Number: e0191673 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850