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 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.

KEYWORDS:

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

Abbreviations

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Acknowledgments

We would like to thank Laurens R. Krol and Thorsten O. Zander for sharing their dataset with us.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the NSF [IIS 1219200, 1817226, 1528214]; DAAD [short-term research grant]; UC San Diego [Chancellor’s Research Innovation Scholarships G2171, G3155 and the Mary Anne Fox dissertation year fellowship]; NIH [5T32MH020002-18].