Decoding Steady-State Visual Evoked Potentials From Electrocorticography
| العنوان: | Decoding Steady-State Visual Evoked Potentials From Electrocorticography |
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| المؤلفون: | Paul Boon, Benjamin Wittevrongel, Dirk Van Roost, Alfred Meurs, Marc M. Van Hulle, Mansoureh Fahimi Hnazaee, Leen De Taeye, Evelien Carrette, Flavio Camarrone, Elvira Khachatryan |
| المساهمون: | Faculty of Medicine and Pharmacy, Neuroprotection & Neuromodulation |
| المصدر: | Frontiers in Neuroinformatics, Vol 12 (2018) Frontiers in Neuroinformatics FRONTIERS IN NEUROINFORMATICS |
| بيانات النشر: | Frontiers Media S.A., 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | SELECTION, Steady state (electronics), decoding, Computer science, Speech recognition, 0206 medical engineering, Biomedical Engineering, Neuroscience (miscellaneous), BRAIN-COMPUTER INTERFACES, 02 engineering and technology, Electroencephalography, scalp-EEG, FREQUENCY, MOTOR IMAGERY, beamforming, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, CANONICAL CORRELATION-ANALYSIS, CHANNEL, Medicine and Health Sciences, medicine, EEG, BCI, CCA, Electrocorticography, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, SSVEP, Original Research, Brain–computer interface, medicine.diagnostic_test, SPELLER, 020601 biomedical engineering, ECoG, Computer Science Applications, Noise, cortex, Visual cortex, medicine.anatomical_structure, ICT, PROSTHETIC DEVICES, 030217 neurology & neurosurgery, Decoding methods, RESPONSES |
| الوصف: | 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. ispartof: Frontiers in Neuroinformatics vol:12 ispartof: location:Switzerland status: published |
| وصف الملف: | application/pdf; Electronic-eCollection |
| اللغة: | English |
| تدمد: | 1662-5196 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edd2a4ac7e67f214c32be003885c719aTest https://www.frontiersin.org/article/10.3389/fninf.2018.00065/fullTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....edd2a4ac7e67f214c32be003885c719a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16625196 |
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