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A Fast SSVEP-Based Brain-Computer Interface

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

Literature of brain-computer interfacing (BCI) for steady-state visual evoked potentials (SSVEP) shows that canonical correlation analysis (CCA) is the most used method to extract features. However, it is known that CCA tends to rapidly overfit, leading to a decrease in performance. Furthermore, CCA uses information of just one class, thus neglecting possible overlaps between different classes. In this paper we propose a new pipeline for SSVEP-based BCIs, called corrLDA, that calculates correlation values between SSVEP signals and sine-cosine reference templates. These features are then reduced with a supervised method called shrinkage linear discriminant analysis that, unlike CCA, can deal with shorter time windows and includes between-class information. To compare these two techniques, we analysed an open access SSVEP dataset from 24 subjects where four stimuli were used in offline and online tasks. The online task was performed both in control condition and under different perturbations: listening, speaking and thinking. Results showed that corrLDA pipeline outperforms CCA in short trial lengths, as well as in the four additional noisy conditions.

This research was supported by MINECO (RYC-2014-15671).

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Acknowledgements

We are grateful to Zafer İşcan for partly sharing code and to Mina Jamshidi and Vadim V. Nikulin for fruitful discussions.

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Authors and Affiliations

  1. Department of Statistics, Informatics and Mathematics, Universidad Pública de Navarra, Pamplona, Spain

    Tania Jorajuría, Marisol Gómez & Carmen Vidaurre

Authors
  1. Tania Jorajuría
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  2. Marisol Gómez
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  3. Carmen Vidaurre
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Corresponding author

Correspondence to Carmen Vidaurre .

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Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Jorajuría, T., Gómez, M., Vidaurre, C. (2020). A Fast SSVEP-Based Brain-Computer Interface. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_5

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