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Spatio-temporal analysis of error-related brain activity in active and passive brain–computer interfaces

Spatio-temporal analysis of error-related brain activity in active and passive brain–computer... Electroencephalography (EEG)-based brain–computer interface (BCI) systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial in both active and passive BCIs. In this work, we propose CREST: a novel covariance-based method that uses Riemannian and Euclidean geometry and combines spatial and temporal aspects of the feedback-related brain activity in response to BCI error. We evaluate our proposed method with two datasets: an active BCI for 1-D cursor control using motor imagery and a passive BCI for 2-D cursor control. We show significant improvement across participants in both datasets compared to existing methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Spatio-temporal analysis of error-related brain activity in active and passive brain–computer interfaces

Brain-Computer Interfaces , Volume 6 (4): 10 – Oct 2, 2019

Spatio-temporal analysis of error-related brain activity in active and passive brain–computer interfaces

Abstract

Electroencephalography (EEG)-based brain–computer interface (BCI) systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial...
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Publisher
Taylor & Francis
Copyright
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2019.1671040
Publisher site
See Article on Publisher Site

Abstract

Electroencephalography (EEG)-based brain–computer interface (BCI) systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial in both active and passive BCIs. In this work, we propose CREST: a novel covariance-based method that uses Riemannian and Euclidean geometry and combines spatial and temporal aspects of the feedback-related brain activity in response to BCI error. We evaluate our proposed method with two datasets: an active BCI for 1-D cursor control using motor imagery and a passive BCI for 2-D cursor control. We show significant improvement across participants in both datasets compared to existing methods.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2019

Keywords: Brain-computer interfaces; electroencephalography; passive BCI; BCI feedback; error-related potentials; error-related spectral perturbation; Riemannian geometry; common spatial patterns; common temporal patterns

References