TY - GEN
T1 - A Fast SSVEP-Based Brain-Computer Interface
AU - Jorajuría, Tania
AU - Gómez, Marisol
AU - Vidaurre, Carmen
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Brain-computer interface
KW - Canonical correlation analysis
KW - Linear discriminant analysis
KW - Steady-state visual evoked potential
UR - https://www.scopus.com/pages/publications/85097111427
U2 - 10.1007/978-3-030-61705-9_5
DO - 10.1007/978-3-030-61705-9_5
M3 - Conference contribution
AN - SCOPUS:85097111427
SN - 9783030617042
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 60
BT - Hybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings
A2 - de la Cal, Enrique Antonio
A2 - Villar Flecha, José Ramón
A2 - Quintián, Héctor
A2 - Corchado, Emilio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020
Y2 - 11 November 2020 through 13 November 2020
ER -