Detection of Steady-State Visual Evoked Potentials based on the Multisignal Classification Algorithm

In this work we evaluated a method for detection of steady-state visual evoked potentials in one-second EEG recordings, based on the multisignal classification (MUSIC) algorithm and support vector machine classification. Three experiments were carried out to test the performance of the method and its applicability for BCI related tasks. The first experiment showed the advantages of using pseudo-spectral features derived from MUSIC over DFT-based detection, using synthetic data within a range of SNR values. A second experiment tested classification of pseudo-spectral features in a dual checkerboard stimuli condition. Finally, a third experiment with ten subjects included an additional no-stimulus condition to be detected. Results showed a faster and more accurate performance for the two- and three-class problems than previously reported DFT-based approaches.