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Improving bit rate in an auditory BCI: Exploiting error-related potentials

Improving bit rate in an auditory BCI: Exploiting error-related potentials The error-related potential (ErrP) can inform the correction of brain-computer interface (BCI) mistakes, but it has thus far been incorporated only into visual BCIs, with mixed success. Given that ErrPs are thought to have higher impact when BCI accuracy is relatively low, we sought to identify the aurally evoked ErrP and investigate its auto-corrective value in auditory BCIs, which typically yield lower accuracies than visual BCIs. We implemented an auditory P300 BCI with four selectable items. Each of nine typically developed participants attempted to spell letter sequences on two separate days. Erroneous feedback was detected by (i) making use of the ErrP, (ii) assessing BCI selection confidence, and (iii) combining these two pieces of information into a hybrid detector. ErrPs were detected with an average cross-validation area under the curve of 0.946. Simulated automatic correction by reverting to the second-ranked letter improved participant-wise information transfer rate by 2.3 bits/minute when errors were detected by the hybrid method. The results suggest ErrP-based error correction can be used to make a substantial improvement in the performance of auditory BCIs. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Improving bit rate in an auditory BCI: Exploiting error-related potentials

Improving bit rate in an auditory BCI: Exploiting error-related potentials

Abstract

The error-related potential (ErrP) can inform the correction of brain-computer interface (BCI) mistakes, but it has thus far been incorporated only into visual BCIs, with mixed success. Given that ErrPs are thought to have higher impact when BCI accuracy is relatively low, we sought to identify the aurally evoked ErrP and investigate its auto-corrective value in auditory BCIs, which typically yield lower accuracies than visual BCIs. We implemented an auditory P300 BCI with four selectable...
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Publisher
Taylor & Francis
Copyright
© 2016 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2016.1169723
Publisher site
See Article on Publisher Site

Abstract

The error-related potential (ErrP) can inform the correction of brain-computer interface (BCI) mistakes, but it has thus far been incorporated only into visual BCIs, with mixed success. Given that ErrPs are thought to have higher impact when BCI accuracy is relatively low, we sought to identify the aurally evoked ErrP and investigate its auto-corrective value in auditory BCIs, which typically yield lower accuracies than visual BCIs. We implemented an auditory P300 BCI with four selectable items. Each of nine typically developed participants attempted to spell letter sequences on two separate days. Erroneous feedback was detected by (i) making use of the ErrP, (ii) assessing BCI selection confidence, and (iii) combining these two pieces of information into a hybrid detector. ErrPs were detected with an average cross-validation area under the curve of 0.946. Simulated automatic correction by reverting to the second-ranked letter improved participant-wise information transfer rate by 2.3 bits/minute when errors were detected by the hybrid method. The results suggest ErrP-based error correction can be used to make a substantial improvement in the performance of auditory BCIs.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Apr 2, 2016

Keywords: Electroencephalography; brain-computer interface (BCI); error-related potential; auditory P300 BCI

References