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

This dataset contains multichannel EEG recordings collected during overt and covert (inner) articulation of six spatial-direction words. Data were acquired from 22 neurologically healthy volunteers: 12 native Russian speakers and 10 native Spanish speakers, aged 22–43 years.

Recording setup: Monopolar EEG signals were recorded from 38 electrodes positioned according to the international 10–10 system, alongside simultaneous EMG recordings from the masseter muscle and laryngeal region. Data were acquired using a Neurovisor-BMM-52 (NVX) EEG system at a sampling rate of 500 Hz.

Experimental task: Participants performed overt and covert articulation of six directional words: "up," "down," "left," "right," "forward," and "backward" (Russian: вверх, вниз, влево, вправо, вперёд, назад; Spanish: arriba, abajo, derecha, izquierda, adelante, atrás). Each participant produced approximately 110 ± 20 trials per word across both conditions.

Dataset contents:

Raw continuous EEG/EMG recordings in .edf format (38 EEG)

Preprocessed epoched data in .fif format (MNE Epochs objects, dimensions: trials × 38 channels × 750 time points)

Event files in .xlsx format with trial timestamps and condition labels

Participant metadata in subject_metadata.json (age, native language)

ANOVA results for spectral power analysis across conditions

Structure:Inner Speech Dataset/├── Spanish/│   ├── sub0/│   │   ├── sub0-epo.fif│   │   ├── sub0.edf│   │   └── sub0.xlsx│   ├── ...│   └── sub9/│       ├── sub9-epo.fif│       ├── sub9.edf│       └── sub9.xlsx├── Russian/│   ├── sub1/│   │   ├── sub1-epo.fif│   │   ├── sub1.edf│   │   └── sub1.xlsx│   ├── ...│   └── sub12/│       ├── sub12-epo.fif│       ├── sub12.edf│       └── sub12.xlsx└── subject_metadata.json

Preprocessing: Bandpass filtering (1–70 Hz), 50 Hz notch filter, ICA-based artifact removal.

Ethics: The study was approved by the Committee of Bioethics of Southern Federal University (Report No. 3, Application No. 7, dated September 9, 2022). A consent waiver for open data sharing was granted, confirming that individual participant consent is not required as the dataset contains no direct identifiers.

Potential applications: Development and benchmarking of BCI algorithms based on inner speech, cross-linguistic EEG analysis, neurolinguistic research, real-time neurocommunication systems.