10.3389/fninf.2018.00065.s001 Benjamin Wittevrongel Benjamin Wittevrongel Elvira Khachatryan Elvira Khachatryan Mansoureh Fahimi Hnazaee Mansoureh Fahimi Hnazaee Flavio Camarrone Flavio Camarrone Evelien Carrette Evelien Carrette Leen De Taeye Leen De Taeye Alfred Meurs Alfred Meurs Paul Boon Paul Boon Dirk Van Roost Dirk Van Roost Marc M. Van Hulle Marc M. Van Hulle Data_Sheet_1_Decoding Steady-State Visual Evoked Potentials From Electrocorticography.PDF Frontiers 2018 BCI ECoG scalp-EEG SSVEP decoding beamforming CCA cortex 2018-09-26 06:12:44 Dataset https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Decoding_Steady-State_Visual_Evoked_Potentials_From_Electrocorticography_PDF/7132799 <p>We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency- and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency- and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG- and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding benefits from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suffice. This study shows, for the first time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes.</p>