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Decoding steady-state visual evoked potentials from electrocorticography

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Abstract
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 bene fi ts 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 suf fi ce. This study shows, for the fi rst 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.
Keywords
BRAIN-COMPUTER INTERFACES, CANONICAL CORRELATION-ANALYSIS, CHANNEL, SELECTION, PROSTHETIC DEVICES, MOTOR IMAGERY, EEG, FREQUENCY, BCI, RESPONSES, SPELLER, BCI, ECoG, scalp-EEG, SSVEP, decoding, beamforming, CCA, cortex

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MLA
Wittevrongel, Benjamin, Elvira Khachatryan, Mansoureh Fahimi Hnazaee, et al. “Decoding Steady-state Visual Evoked Potentials from Electrocorticography.” FRONTIERS IN NEUROINFORMATICS 12 (2018): n. pag. Print.
APA
Wittevrongel, B., Khachatryan, E., Hnazaee, M. F., Camarrone, F., Carrette, E., De Taeye, L., Meurs, A., et al. (2018). Decoding steady-state visual evoked potentials from electrocorticography. FRONTIERS IN NEUROINFORMATICS, 12.
Chicago author-date
Wittevrongel, Benjamin, Elvira Khachatryan, Mansoureh Fahimi Hnazaee, Flavio Camarrone, Evelien Carrette, Leen De Taeye, Alfred Meurs, Paul Boon, Dirk Van Roost, and Marc M Van Hulle. 2018. “Decoding Steady-state Visual Evoked Potentials from Electrocorticography.” Frontiers in Neuroinformatics 12.
Chicago author-date (all authors)
Wittevrongel, Benjamin, Elvira Khachatryan, Mansoureh Fahimi Hnazaee, Flavio Camarrone, Evelien Carrette, Leen De Taeye, Alfred Meurs, Paul Boon, Dirk Van Roost, and Marc M Van Hulle. 2018. “Decoding Steady-state Visual Evoked Potentials from Electrocorticography.” Frontiers in Neuroinformatics 12.
Vancouver
1.
Wittevrongel B, Khachatryan E, Hnazaee MF, Camarrone F, Carrette E, De Taeye L, et al. Decoding steady-state visual evoked potentials from electrocorticography. FRONTIERS IN NEUROINFORMATICS. 2018;12.
IEEE
[1]
B. Wittevrongel et al., “Decoding steady-state visual evoked potentials from electrocorticography,” FRONTIERS IN NEUROINFORMATICS, vol. 12, 2018.
@article{8580450,
  abstract     = {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 bene fi ts 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 suf fi ce. This study shows, for the fi rst 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.},
  articleno    = {65},
  author       = {Wittevrongel, Benjamin and Khachatryan, Elvira and Hnazaee, Mansoureh Fahimi and Camarrone, Flavio and Carrette, Evelien and De Taeye, Leen and Meurs, Alfred and Boon, Paul and Van Roost, Dirk and Van Hulle, Marc M},
  issn         = {1662-5196},
  journal      = {FRONTIERS IN NEUROINFORMATICS},
  keywords     = {BRAIN-COMPUTER INTERFACES,CANONICAL CORRELATION-ANALYSIS,CHANNEL,SELECTION,PROSTHETIC DEVICES,MOTOR IMAGERY,EEG,FREQUENCY,BCI,RESPONSES,SPELLER,BCI,ECoG,scalp-EEG,SSVEP,decoding,beamforming,CCA,cortex},
  language     = {eng},
  pages        = {14},
  title        = {Decoding steady-state visual evoked potentials from electrocorticography},
  url          = {http://dx.doi.org/10.3389/fninf.2018.00065},
  volume       = {12},
  year         = {2018},
}

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