TY - GEN
T1 - Filter bank approach for enhancement of supervised Canonical Correlation Analysis methods for SSVEP-based BCI spellers
AU - Bolanos, Mario Corral
AU - Barrado Ballestero, Sheyla
AU - Puthusserypady, Sadasivan
PY - 2021
Y1 - 2021
N2 - Canonical correlation analysis (CCA) is one of the most used algorithms in the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems due to its simplicity, efficiency, and robustness. Researchers have proposed modifications to CCA to improve its speed, allowing high-speed spelling and thus a more natural communication. In this work, we combine two approaches, the filter-bank (FB) approach to extract more information from the harmonics, and a range of different supervised methods which optimize the reference signals to improve the SSVEP detection. The proposed models are tested on the publicly available benchmark dataset for SSVEP-based BCIs and the results show improved performance compared to the state-of-the-art methods and, in particular, the proposed FBMwayCCA approach achieves the best results with an information transfer rate (ITR) of 134.8±8.4 bits/minute. This study indeed suggests the feasibility of combining the fundamental and harmonic SSVEP components with supervised methods in target identification to develop high-speed BCI spellers.
AB - Canonical correlation analysis (CCA) is one of the most used algorithms in the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems due to its simplicity, efficiency, and robustness. Researchers have proposed modifications to CCA to improve its speed, allowing high-speed spelling and thus a more natural communication. In this work, we combine two approaches, the filter-bank (FB) approach to extract more information from the harmonics, and a range of different supervised methods which optimize the reference signals to improve the SSVEP detection. The proposed models are tested on the publicly available benchmark dataset for SSVEP-based BCIs and the results show improved performance compared to the state-of-the-art methods and, in particular, the proposed FBMwayCCA approach achieves the best results with an information transfer rate (ITR) of 134.8±8.4 bits/minute. This study indeed suggests the feasibility of combining the fundamental and harmonic SSVEP components with supervised methods in target identification to develop high-speed BCI spellers.
U2 - 10.1109/EMBC46164.2021.9630838
DO - 10.1109/EMBC46164.2021.9630838
M3 - Article in proceedings
C2 - 34891304
SN - 978-1-7281-1180-3
T3 - Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference
SP - 337
EP - 340
BT - Proceedings of 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PB - IEEE
T2 - 43<sup>rd</sup> Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Y2 - 1 November 2021 through 5 November 2021
ER -