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VEPCON is a multimodal neuroimaging dataset comprising high-density EEG, structural MRI, and diffusion-weighted imaging from 20 participants performing visual discrimination tasks (face vs. scrambled faces, coherent vs. incoherent motion). The dataset includes preprocessed EEG single trials, individual brain parcellations at five spatial resolutions, structural connectomes derived from diffusion data, and EEG source imaging solutions based on individual anatomy. This resource supports multimodal methods development, structure-function relationship studies, and optimization of source imaging and graph analysis techniques.

Participants

20

Channels

128 (biosemi)

Citations

24

Size

58.2 GB

Version

v1.0.0

Updated

Jul 22, 2026

Imported from OpenNeuro ds002578

Participants

2

Channels

256 (biosemi)

HED

v8.0.0

Size

1.33 GB

Version

v1.0.2

Updated

Jul 10, 2026

This dataset comprises scalp EEG recordings from 22 participants who used BrainForm, a gamified brain-computer interface (BCI) designed for P300 event-related potential (ERP) training and scalable data collection with consumer hardware. Participants performed repeated runs of a P300 spelling/selection task, allowing analysis of BCI skill acquisition across sessions and the effects of different visual stimulation textures on performance. The dataset supports research on BCI training, human factors, and machine learning applications on ERP data.

Participants

22

Channels

8 (10-10)

Citations

1

HED

v8.4.0

Size

237 MB

Version

v1.0.6

Updated

Aug 20, 2026

This dataset comprises high-density electroencephalography (EEG) recordings acquired during a visual stimulation task from human participants. Data were collected in a single session and organized according to the EEG-BIDS specification, with raw recordings stored in BrainVision format. The dataset provides raw, unfiltered EEG signals suitable for subsequent offline preprocessing and analysis of visual evoked responses.

Participants

31

Channels

64

Citations

1

Size

17.6 GB

Version

v1.0.0

Updated

Aug 19, 2026

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants performing left and right hand motor imagery in response to visual cues. Recorded at 1000 Hz using a 64-channel EEG montage with standardized electrode placement, this dataset supports brain-computer interface research and analysis of ocular activity patterns during motor imagery paradigms.

Participants

31

Channels

62 (10-10)

Citations

11

HED

v8.4.0

Size

33.6 GB

Version

v1.0.3

Updated

Aug 18, 2026

A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.

Participants

10

Channels

124 (egi-geodesic)

Citations

93

HED

v8.4.0

Size

3.40 GB

Version

v1.0.3

Updated

Aug 18, 2026

Participants

20

Channels

32 (10-10)

Citations

1

Size

7.75 GB

Version

v1.0.0

Updated

Jul 10, 2026

The MNE-Sample-Data dataset comprises simultaneous MEG and EEG recordings acquired from a single subject using a Neuromag Vectorview system at the Martinos Center for Biomedical Imaging. The experiment involved visual stimulation (checkerboard patterns presented to left and right visual fields) and auditory stimulation (tones to left and right ears), with occasional face stimuli requiring motor responses. Structural MRI data from a 1.5 T Siemens scanner and Freesurfer-derived anatomical derivatives are included, providing a comprehensive reference dataset for MEG/EEG analysis and method development.

Participants

2

Size

1.93 GB

Version

v1.0.0

Updated

Jul 10, 2026

The BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels plus 3 EOG channels (25 total) sampled at 250 Hz with minimal preprocessing (bandpass filtering 0.05-200 Hz), making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.

Participants

9

Channels

22 (10-10)

Citations

90

HED

v8.4.0

Size

1.38 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from a P300 visual matrix speller study comparing three unsupervised learning methods (Expectation-Maximization, Learning from Label Proportions, and their combination MIX) for brain-computer interface decoding. Twelve healthy participants performed a copy-spelling task using a modified 6×6 character grid extended with 10 # symbols as visual blanks (46 total symbols), recorded at 1000 Hz from 31 EEG channels. The study demonstrates that unsupervised learning methods can achieve performance comparable to supervised approaches without requiring calibration data.

Participants

12

Channels

31 (10-10)

Citations

26

HED

v8.4.0

Size

4.81 GB

Version

v1.0.2

Updated

Aug 18, 2026