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Sensors 2018, 18(3), 794; https://doi.org/10.3390/s18030794

Accurate Decoding of Short, Phase-Encoded SSVEPs

1
Electrical Engineering (ESAT) TC, Campus Group-T Leuven, Division Animal and Human Health Engineering, KU Leuven, 3000 Leuven, Belgium
2
Department of Neurosciences, Laboratory for Neuro- & Psychophysiology, KU Leuven, 3000 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Received: 25 October 2017 / Revised: 27 February 2018 / Accepted: 2 March 2018 / Published: 6 March 2018
(This article belongs to the Section Intelligent Sensors)
Full-Text   |   PDF [336 KB, uploaded 6 March 2018]   |  

Abstract

Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain–computer interfacing (BCI) is assessed. The novel features are based on phase estimation and correlations between target responses. The targets are decoded from the feature scores using the least squares support vector machine (LS-SVM) classifier, and it is shown that some of the proposed features compete with state-of-the-art classifiers when using short (0.5 s) EEG recordings in a binary classification setting. View Full-Text
Keywords: BCI; EEG; SSVEP BCI; EEG; SSVEP
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Youssef Ali Amer, A.; Wittevrongel, B.; Van Hulle, M.M. Accurate Decoding of Short, Phase-Encoded SSVEPs. Sensors 2018, 18, 794.

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