Authors:
Rosanne Zerafa
1
;
Tracey Camilleri
1
;
Owen Falzon
2
and
Kenneth P. Camilleri
1
;
2
Affiliations:
1
Department of Systems and Control Engineering, Faculty of Engineering, University of Malta, Msida, Malta
;
2
Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
Keyword(s):
Steady-State Visually Evoked Potential, BCI, Electroencephalography, Single-channel, Univariate, Multiple Modelling, Autoregressive Modelling.
Abstract:
This work investigates a novel autoregressive multiple model (AR-MM) probabilistic framework for the detection of steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The proposed
method is compared to standard SSVEP detection techniques using a 12-class SSVEP dataset recorded from
10 subjects. The results, obtained from a single-channel analysis, reveal that the AR-MM probabilistic framework significantly improves the SSVEP detection performance compared to the standard single-channel power
spectral density analysis (PSDA) method. Specifically, an average classification accuracy of 82.02 ± 16.21
% and an information transfer rate (ITR) of 48.22 ± 17.25 bpm are obtained with a 2 s period for SSVEP
detection with the AR-MM probabilistic framework. These results are found to be on average only 2.29 %
and 3.73 % lower in classification accuracy compared to the state-of-the-art multichannel SSVEP detection
methods, specifically the canonical correlat
ion analysis (CCA) and the filter bank canonical correlation analysis (FBCCA) methods, respectively. In terms of training, it is shown that the proposed approach requires only
a few seconds of data to train each model. This study revealed the potential of using the AR-MM probabilistic
approach to distinguish between different classes using single-channel SSVEP data. The proposed method is
particularly appealing for practical use in real-world BCI applications where a minimal amount of channels
and training data are desirable.
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