Prediction of P300 BCI Aptitude in Severe Motor Impairment

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a...

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Main Authors:	Halder, Sebastian, Ruf, Carolin Anne, Furdea, Adrian, Pasqualotto, Emanuele, De Massari, Daniele, van der Heiden, Linda, Bogdan, Martin, Rosenstiel, Wolfgang, Birbaumer, Niels, Kübler, Andrea, Matuz, Tamara
Format:	Online
Language:	English
Published:	Public Library of Science 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799852/

id	pubmed-3799852
recordtype	oai_dc
spelling	pubmed-37998522013-11-07 Prediction of P300 BCI Aptitude in Severe Motor Impairment Halder, Sebastian Ruf, Carolin Anne Furdea, Adrian Pasqualotto, Emanuele De Massari, Daniele van der Heiden, Linda Bogdan, Martin Rosenstiel, Wolfgang Birbaumer, Niels Kübler, Andrea Matuz, Tamara Research Article Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = −0.77) and of the N2 (r = −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance. Public Library of Science 2013-10-18 /pmc/articles/PMC3799852/ /pubmed/24204597 http://dx.doi.org/10.1371/journal.pone.0076148 Text en © 2013 Halder et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Halder, Sebastian Ruf, Carolin Anne Furdea, Adrian Pasqualotto, Emanuele De Massari, Daniele van der Heiden, Linda Bogdan, Martin Rosenstiel, Wolfgang Birbaumer, Niels Kübler, Andrea Matuz, Tamara
spellingShingle	Halder, Sebastian Ruf, Carolin Anne Furdea, Adrian Pasqualotto, Emanuele De Massari, Daniele van der Heiden, Linda Bogdan, Martin Rosenstiel, Wolfgang Birbaumer, Niels Kübler, Andrea Matuz, Tamara Prediction of P300 BCI Aptitude in Severe Motor Impairment
author_facet	Halder, Sebastian Ruf, Carolin Anne Furdea, Adrian Pasqualotto, Emanuele De Massari, Daniele van der Heiden, Linda Bogdan, Martin Rosenstiel, Wolfgang Birbaumer, Niels Kübler, Andrea Matuz, Tamara
author_sort	Halder, Sebastian
title	Prediction of P300 BCI Aptitude in Severe Motor Impairment
title_short	Prediction of P300 BCI Aptitude in Severe Motor Impairment
title_full	Prediction of P300 BCI Aptitude in Severe Motor Impairment
title_fullStr	Prediction of P300 BCI Aptitude in Severe Motor Impairment
title_full_unstemmed	Prediction of P300 BCI Aptitude in Severe Motor Impairment
title_sort	prediction of p300 bci aptitude in severe motor impairment
description	Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = −0.77) and of the N2 (r = −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.
publisher	Public Library of Science
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3799852/
_version_	1612019221910257664

Prediction of P300 BCI Aptitude in Severe Motor Impairment

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