Academic Journal
EEG-based brain-computer interface (BCI) dataset for directional word recognition.
| Τίτλος: | EEG-based brain-computer interface (BCI) dataset for directional word recognition. |
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| Συγγραφείς: | Kostulin DV; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia. kostyulin@sfedu.ru., Shaposhnikov PD; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia., Ekizyan AK; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia., Shevchenko IG; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia., Shaposhnikov DG; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia., Shcherban IV; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia., Kiroy VN; Research Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia. |
| Πηγή: | Scientific data [Sci Data] 2026 Aug 17; Vol. 13 (1). Date of Electronic Publication: 2026 Aug 17. |
| Τύπος έκδοσης: | Journal Article; Dataset |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, 2014- |
| Ιατρικοί όροι (MeSH): | Brain-Computer Interfaces* , Electroencephalography*, Humans ; Electromyography ; Machine Learning |
| Περίληψη: | We present an EEG dataset recorded from 22 neurologically healthy volunteers (12 native Russian speakers and 10 native Spanish speakers) during overt and covert articulation of six spatial-direction words. Monopolar EEG signals were acquired from 38 electrodes positioned according to the international 10-10 system using a Neurovisor-BMM-52 (NVX) amplifier at 500 Hz. In a subset of participants, electromyography (EMG) was simultaneously recorded from the masseter muscle and laryngeal region to exploratorily characterize articulatory muscle activation. Exploratory spectral and coherence analyses, together with classification using standard machine learning methods (Random Forest, SVM, LDA), confirm the presence of condition-specific neural activity distinguishable by standard classifiers (best accuracy 78 ± 4%). The dataset is intended to support the development and benchmarking of algorithms for inner speech recognition in brain-computer interface applications. (© 2026. The Author(s).) |
| Competing Interests: | Competing interests: The authors declare no competing interests. |
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| Entry Date(s): | Date Created: 20260817 Date Completed: 20260817 Latest Revision: 20260820 |
| Update Code: | 20260820 |
| PubMed Central ID: | PMC13482365 |
| DOI: | 10.1038/s41597-026-07809-9 |
| PMID: | 42608413 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2052-4463 |
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| DOI: | 10.1038/s41597-026-07809-9 |