An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a r...

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Main Authors: Yeom, Seul-Ki, Fazli, Siamac, Müller, Klaus-Robert, Lee, Seong-Whan
Format: Online
Language:English
Published: Public Library of Science 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226481/
id pubmed-4226481
recordtype oai_dc
spelling pubmed-42264812014-11-13 An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity Yeom, Seul-Ki Fazli, Siamac Müller, Klaus-Robert Lee, Seong-Whan Research Article Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup. Public Library of Science 2014-11-10 /pmc/articles/PMC4226481/ /pubmed/25384045 http://dx.doi.org/10.1371/journal.pone.0111157 Text en © 2014 Yeom 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 Yeom, Seul-Ki
Fazli, Siamac
Müller, Klaus-Robert
Lee, Seong-Whan
spellingShingle Yeom, Seul-Ki
Fazli, Siamac
Müller, Klaus-Robert
Lee, Seong-Whan
An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity
author_facet Yeom, Seul-Ki
Fazli, Siamac
Müller, Klaus-Robert
Lee, Seong-Whan
author_sort Yeom, Seul-Ki
title An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity
title_short An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity
title_full An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity
title_fullStr An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity
title_full_unstemmed An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity
title_sort efficient erp-based brain-computer interface using random set presentation and face familiarity
description Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.
publisher Public Library of Science
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226481/
_version_ 1613154469290180608

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