Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces

Abu-Alqumsan, M. and Peer, A. (2016) Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces. Journal of Neural Engineering, 13 (3). ISSN 1741-2560 Available from: http://eprints.uwe.ac.uk/29137

[img]
Preview
PDF - Accepted Version
Available under License All Rights Reserved.

4MB

Publisher's URL: http://dx.doi.org/10.1088/1741-2560/13/3/036005

Abstract/Description

Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired EEG data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index (MSI) method and the maximum contrast combination (MCC) method are variations of the CCA method. All these three methods were found to provide relatively unreliable detections in low SNR regimes. CVARS and the minimum energy combination (MEC) methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

Item Type:Article
Uncontrolled Keywords:BCI, SSVEP, canonical correlation analysis, minimum energy combination, multivariate synchronization index, maximum contrast combination, autoregressive spectral analysis
Faculty/Department:Faculty of Environment and Technology > Department of Engineering Design and Mathematics
ID Code:29137
Deposited By: Professor A. Peer
Deposited On:15 Jun 2016 14:00
Last Modified:16 Jul 2017 10:59

Request a change to this item

Total Document Downloads in Past 12 Months

Document Downloads

Total Document Downloads

More statistics for this item...