EEG-based brain-computer interface (BCI) dataset for directional word recognition.

D V Kostulin, P D Shaposhnikov, A Kh Ekizyan, I G Shevchenko, D G Shaposhnikov, I V Shcherban, V N Kiroy

Abstract readDataset

In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

EEG-based brain-computer interface (BCI) dataset for directional word recognition.Scientific data · 2026
Article

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5 · Who and what money

Authors and funding

7 authors.

D V KostulinResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia. kostyulin@sfedu.ru.

P D ShaposhnikovResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia.

A Kh EkizyanResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia.

I G ShevchenkoResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia.

D G ShaposhnikovResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia.

I V ShcherbanResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia.

V N KiroyResearch Center of Neurotechnologies, Southern Federal University, Prospekt Stachki 194 avenue, Rostov-on-Don, 344090, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Brain-Computer InterfacesElectroencephalographyElectromyographyHumansMachine Learning

Identifiers

PMID42608413

DOI10.1038/s41597-026-07809-9

PMCPMC13482365

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.