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Gaze-independent ERP-BCIs: augmenting performance through location-congruent bimodal stimuli

Overview of attention for article published in Frontiers in Systems Neuroscience, September 2014
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Title
Gaze-independent ERP-BCIs: augmenting performance through location-congruent bimodal stimuli
Published in
Frontiers in Systems Neuroscience, September 2014
DOI 10.3389/fnsys.2014.00143
Pubmed ID
Authors

Marieke E. Thurlings, Anne-Marie Brouwer, Jan B. F. Van Erp, Peter Werkhoven

Abstract

Gaze-independent event-related potential (ERP) based brain-computer interfaces (BCIs) yield relatively low BCI performance and traditionally employ unimodal stimuli. Bimodal ERP-BCIs may increase BCI performance due to multisensory integration or summation in the brain. An additional advantage of bimodal BCIs may be that the user can choose which modality or modalities to attend to. We studied bimodal, visual-tactile, gaze-independent BCIs and investigated whether or not ERP components' tAUCs and subsequent classification accuracies are increased for (1) bimodal vs. unimodal stimuli; (2) location-congruent vs. location-incongruent bimodal stimuli; and (3) attending to both modalities vs. to either one modality. We observed an enhanced bimodal (compared to unimodal) P300 tAUC, which appeared to be positively affected by location-congruency (p = 0.056) and resulted in higher classification accuracies. Attending either to one or to both modalities of the bimodal location-congruent stimuli resulted in differences between ERP components, but not in classification performance. We conclude that location-congruent bimodal stimuli improve ERP-BCIs, and offer the user the possibility to switch the attended modality without losing performance.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
United States 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 20%
Student > Ph. D. Student 6 20%
Student > Master 5 17%
Student > Doctoral Student 3 10%
Student > Bachelor 3 10%
Other 5 17%
Unknown 2 7%
Readers by discipline Count As %
Engineering 10 33%
Psychology 5 17%
Computer Science 4 13%
Agricultural and Biological Sciences 3 10%
Neuroscience 2 7%
Other 3 10%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 July 2014.
All research outputs
#18,375,064
of 22,758,963 outputs
Outputs from Frontiers in Systems Neuroscience
#1,127
of 1,340 outputs
Outputs of similar age
#170,165
of 238,606 outputs
Outputs of similar age from Frontiers in Systems Neuroscience
#51
of 62 outputs
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