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 in the subset of subjects 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/├── README.md├── manifest.csv├── data_dictionary.md├── raw/│   ├── russian/│   │   ├── sub1/ ... sub12/│   │   │   └── sub1_session.edf│   │   └── spanish/│   │       ├── sub0/ ... sub9/│   │       │   └── sub0_session.edf├── preprocessed/│   ├── russian/│   │   ├── sub1/ ... sub12/│   │   │   ├── sub1_epochs.fif│   │   │   └── sub1_events.xlsx│   │   └── spanish/│   │       ├── sub0/ ... sub9/│   │       │   ├── sub0_epochs.fif│   │       │   └── sub0_events.xlsx└── metadata/    └── 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.