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>