Next Article in Journal
A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis
Previous Article in Journal
EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State
Open AccessArticle

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.
Sensors 2018, 18(3), 794; https://doi.org/10.3390/s18030794
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
Keywords: BCI; EEG; SSVEP BCI; EEG; SSVEP
Show Figures

Figure 1

MDPI and ACS Style

Youssef Ali Amer, A.; Wittevrongel, B.; Van Hulle, M.M. Accurate Decoding of Short, Phase-Encoded SSVEPs. Sensors 2018, 18, 794.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop