Accurate Decoding of Short, Phase-Encoded SSVEPs

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.