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A 32-channel EEG dataset comprising visual imagery recordings from 22 healthy participants performing mental imagery tasks of 10 distinct categories (animals, geometric figures, and objects). The dataset contains 16,800 trials collected across 2 sessions per subject with simultaneous EEG acquisition at 1000 Hz, designed to support brain-computer interface research and classification algorithm development.

Size

31.7 GB

Updated

Jun 3, 2026

Participants

25

This open-access hybrid brain-computer interface dataset combines simultaneous EEG and near-infrared spectroscopy (NIRS) recordings from 29 healthy subjects performing mental arithmetic and motor imagery tasks. Experiment B focuses on mental arithmetic (serial subtraction) versus rest conditions across six sessions with 20 trials per session. The dataset includes preprocessed EEG data (30 channels, 200 Hz sampling rate) with comprehensive artifact correction and has been validated for single-trial classification using common spatial patterns and linear discriminant analysis.

Size

1.87 GB

Updated

Jun 3, 2026

Participants

9

Participants

10

Size

672 MB

Updated

May 16, 2026

Size

152 MB

Updated

May 16, 2026

BigP3BCI Study G is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 checkerboard visual speller task. This derivative dataset is Study G of 20 studies in the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects total. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.

Size

327 MB

Updated

Jun 3, 2026

BigP3BCI Study D is a P300-based brain-computer interface dataset comprising EEG recordings from 17 healthy subjects performing a 6x6 character grid speller task using dynamic row-column stimulus presentation. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 electrode montage, annotated with target and non-target event labels using HED 8.4.0 schema. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is designed for machine learning model development and benchmarking in brain-computer interface research.

Size

731 MB

Updated

Jun 3, 2026

This dataset comprises EEG recordings from 25 healthy participants performing motor imagery and spatial attention tasks across five sessions. The study integrates simultaneous motor imagery (left and right hand) with overt spatial attention in an online brain-computer interface paradigm, featuring 64-channel recordings at 1000 Hz with cursor feedback. The dataset includes 1,875 trials collected during motor imagery, overt spatial attention, and combined tasks, providing a comprehensive resource for investigating the interaction between motor imagery and spatial attention in BCI applications. This is a derivative dataset processed and organized according to BIDS standards from the original study data.

Size

4.89 GB

Updated

Jun 3, 2026