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Open AccessArticle

Accurate Decoding of Short, Phase-Encoded SSVEPs

Electrical Engineering (ESAT) TC, Campus Group-T Leuven, Division Animal and Human Health Engineering, KU Leuven, 3000 Leuven, Belgium
Department of Neurosciences, Laboratory for Neuro- & Psychophysiology, KU Leuven, 3000 Leuven, Belgium
Author to whom correspondence should be addressed.
Sensors 2018, 18(3), 794;
Received: 25 October 2017 / Revised: 27 February 2018 / Accepted: 2 March 2018 / Published: 6 March 2018
(This article belongs to the Section Intelligent Sensors)
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
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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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