An Autoregressive Multiple Model Probabilistic Framework for the Detection of SSVEPs in Brain-Computer Interfaces

Rosanne Zerafa, Tracey Camilleri, Owen Falzon, Kenneth P. Camilleri, Kenneth P. Camilleri

2020

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 correlation 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.

Paper Citation

in Harvard Style

Zerafa R., Camilleri T., Falzon O. and Camilleri K. (2020). An Autoregressive Multiple Model Probabilistic Framework for the Detection of SSVEPs in Brain-Computer Interfaces. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS; ISBN 978-989-758-398-8, SciTePress, pages 68-78. DOI: 10.5220/0008924400680078

in Bibtex Style

@conference{biosignals20,
author={Rosanne Zerafa and Tracey Camilleri and Owen Falzon and Kenneth P. Camilleri},
title={An Autoregressive Multiple Model Probabilistic Framework for the Detection of SSVEPs in Brain-Computer Interfaces},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS},
year={2020},
pages={68-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008924400680078},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS
TI - An Autoregressive Multiple Model Probabilistic Framework for the Detection of SSVEPs in Brain-Computer Interfaces
SN - 978-989-758-398-8
AU - Zerafa R.
AU - Camilleri T.
AU - Falzon O.
AU - Camilleri K.
PY - 2020
SP - 68
EP - 78
DO - 10.5220/0008924400680078
PB - SciTePress