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An open-access hybrid brain-computer interface dataset combining electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings from 29 healthy subjects performing motor imagery and mental arithmetic tasks. The dataset includes preprocessed EEG data (30 EEG channels plus 2 EOG channels, 200 Hz sampling rate) and NIRS measurements (36 channels) across six sessions per subject, with validated classification performance demonstrating enhanced discrimination of mental states through multimodal analysis compared to single-modality approaches.

Participants

29

Channels

30 (10-05)

Citations

94

HED

v8.4.0

Size

6.50 GB

Version

v1.0.3

Updated

Aug 18, 2026

A classical motor imagery EEG dataset comprising recordings from 7 healthy subjects performing left hand, right hand, and passive rest tasks. The dataset contains 19-channel EEG data sampled at 200 Hz with visual arrow cues guiding motor imagery tasks. This benchmark dataset is designed for evaluating brain-computer interface algorithms and motor imagery classification methods.

Participants

7

Channels

19 (10-20)

Citations

81

HED

v8.4.0

Size

1.20 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains behavioral and EEG data from twenty-one participants who learned to perform two drumming sequences (AABA and AAABAA) at three tempos (60, 100, and 150 bpm) using keyboard responses. Trial-by-trial visual feedback indicated timing accuracy relative to an adaptive response window, allowing investigation of motor learning and timing control. The dataset includes detailed event coding for stimulus conditions and feedback types across trials.

Participants

21

Channels

31 (10-10)

Citations

1

Size

4.77 GB

Version

v1.0.0

Updated

Aug 19, 2026