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Online detection of error-related potentials in multi-class cognitive task-based BCIs

Online detection of error-related potentials in multi-class cognitive task-based BCIs One method for improving the accuracy and hence the rate of communication of a brain–computer interface (BCI) is to automatically correct erroneous classifications by exploiting error-related potentials (ErrPs). The merit of such a correction scheme has been demonstrated in both active (e.g. motor imagery) and reactive (e.g. P300) BCIs. Here, we investigated the effect of ErrP-guided error correction in a three-class, active BCI based on cognitive rather than motor imagery tasks using electroencephalography (EEG). Ten able-bodied adults participated in three sessions of data collection. For each participant, a ternary BCI differentiated among idle state and two personally selected cognitive tasks (e.g. mental arithmetic, counting, word generation, and figure rotation). Real-time feedback of the BCI decision was displayed to the participant following each task. EEG data after feedback onset were used to detect ErrPs and correct the BCI’s output in the case of detected errors. ErrP-based error correction modestly but significantly improved the average online task classification accuracy (+7%) as well as the information transfer rate (+0.9 bits/min) of the ternary BCI across participants. Our findings support further study of ErrPs in active BCIs based on cognitive tasks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Online detection of error-related potentials in multi-class cognitive task-based BCIs

Online detection of error-related potentials in multi-class cognitive task-based BCIs

Abstract

One method for improving the accuracy and hence the rate of communication of a brain–computer interface (BCI) is to automatically correct erroneous classifications by exploiting error-related potentials (ErrPs). The merit of such a correction scheme has been demonstrated in both active (e.g. motor imagery) and reactive (e.g. P300) BCIs. Here, we investigated the effect of ErrP-guided error correction in a three-class, active BCI based on cognitive rather than motor imagery tasks using...
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Publisher
Taylor & Francis
Copyright
© 2019 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2019.1614770
Publisher site
See Article on Publisher Site

Abstract

One method for improving the accuracy and hence the rate of communication of a brain–computer interface (BCI) is to automatically correct erroneous classifications by exploiting error-related potentials (ErrPs). The merit of such a correction scheme has been demonstrated in both active (e.g. motor imagery) and reactive (e.g. P300) BCIs. Here, we investigated the effect of ErrP-guided error correction in a three-class, active BCI based on cognitive rather than motor imagery tasks using electroencephalography (EEG). Ten able-bodied adults participated in three sessions of data collection. For each participant, a ternary BCI differentiated among idle state and two personally selected cognitive tasks (e.g. mental arithmetic, counting, word generation, and figure rotation). Real-time feedback of the BCI decision was displayed to the participant following each task. EEG data after feedback onset were used to detect ErrPs and correct the BCI’s output in the case of detected errors. ErrP-based error correction modestly but significantly improved the average online task classification accuracy (+7%) as well as the information transfer rate (+0.9 bits/min) of the ternary BCI across participants. Our findings support further study of ErrPs in active BCIs based on cognitive tasks.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Apr 3, 2019

Keywords: Brain-computer interface; electroencephalography; error-related potentials; cognitive tasks

References