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EEG recordings from five participants with speech and/or physical impairments due to amyotrophic lateral sclerosis (ALS) who used a non-implantable communication brain-computer interface (cBCI) over multiple visits. Participants completed calibration and copy-spelling tasks with Matrix and RSVP layouts, in both customized and non-customized system configurations. Customization was iteratively adjusted based on typing performance, preferences, clinical observations, brain signal characteristics, and offline simulations. The data are organized in BIDS format.

Participants

5

Channels

7 (10-10)

Size

2.17 GB

Version

v1.0.0

Updated

Jun 19, 2026

A calibrated task subset of Kai J. Miller's library of human electrocorticographic (ECoG) data, comprising 111 recordings from 24 epilepsy patients with subdural electrode grids. Tasks include finger flexion, hand gestures, motor movement and imagery, and verb generation and noun reading. Data were recorded at 1 kHz and converted to BIDS in BrainVision format with source-documented amplitude calibration.

Participants

24

Size

14.0 GB

Version

v1.0.0

Updated

Oct 8, 2026

This dataset contains motor-imagery EEG, bipolar EMG, skin temperature and accelerometry recorded from 26 healthy volunteers performing cued left- vs right-hand motor imagery during MI-BCI training in immersive virtual reality. Participants were randomized to an embodiment-primed (virtual hand illusion) group or a non-embodied control group, each completing one run of 40 trials. The data were re-released in BIDS format via MOABB from the original Zenodo deposit.

Participants

26

Channels

32 (10-10)

HED

v8.4.0

Size

3.65 GB

Version

v1.0.1

Updated

Oct 8, 2026

A placeholder EEG dataset named FT, formatted according to BIDS with HED annotations. The README provides no further information about the experimental paradigm, participants, or recording setup. The dataset is a NEMAR-hosted copy of an OpenNeuro deposit.

Participants

1

Channels

64 (10-10)

HED

v8.1.0

Size

79.2 MB

Version

v1.0.0

Updated

May 13, 2026

This dataset contains 32-channel EEG recordings acquired during a sham neurofeedback experiment in a virtual reality environment. The study examined how feedback valence (positive, negative, or none) modulates alpha-band activity during an attentional task. Each participant completed positive, negative, control, and resting-state (eyes open/eyes closed) conditions.

Participants

36

Channels

29 (10-10)

Size

10.4 GB

Version

v1.0.0

Updated

Jun 29, 2026

DataSet1 is an EEG dataset originally deposited on OpenNeuro (ds002791) and mirrored on NEMAR. It was contributed by Mheich, Dufor, Yassine, Kabbara, Wendling and Hassan. The available documentation gives no further detail on the experimental paradigm or participants.

Participants

23

Channels

256 (egi-geodesic)

Size

47.1 GB

Version

v1.0.0

Updated

Jun 21, 2026

This dataset contains simultaneously recorded scalp EEG and intracranial EEG (depth electrodes) from fifteen epilepsy patients undergoing intracranial monitoring while performing a modified Sternberg verbal working memory task. Encoding, maintenance, and recall are temporally separated. It includes electrode MNI coordinates with anatomical labels, trial-level behavioral information, and LCMV beamforming-reconstructed virtual sensor data for several cortical regions. The data support studies of working memory, connectivity, and replay of memory items.

Participants

15

Channels

19 (10-20)

Citations

47

Size

11.9 GB

Version

v1.0.0

Updated

Jun 25, 2026

A sample intracranial EEG (iEEG) dataset prepared for the WIRED ICM course on intracranial recordings in humans, epilepsy, and deep brain stimulation (Paris, March 2024). It contains recordings from patients with epilepsy during naturalistic listening tasks (movie trailers and TIMIT sentences). It also includes illustrative examples of seizure-related activity and vagus nerve stimulator artifact.

Participants

3

Size

348 MB

Version

v1.0.0

Updated

Jun 26, 2026

This dataset contains EEG recordings from 9 healthy subjects performing cued motor imagery of left hand, right hand, feet, and tongue movements, known as BCI Competition IV Dataset 2a. Each subject completed two sessions of six runs, with 22 EEG and 3 EOG channels sampled at 250 Hz. It is widely used as a benchmark for multi-class motor imagery classification in brain-computer interface research.

Participants

9

Channels

22 (10-10)

Citations

89

HED

v8.4.0

Size

1.38 GB

Version

v1.0.3

Updated

Oct 8, 2026

Electroencephalographic recordings from 14 healthy participants performing a cue-guided, two-class motor imagery task (right hand versus feet) in a Graz-BCI paradigm with continuous visual feedback. Each participant completed a single session of 8 runs and 160 trials, recorded with 15 EEG channels at 512 Hz. The dataset was used to compare random forest and regularized LDA classifiers for sensorimotor rhythm brain-computer interfaces.

Participants

14

Channels

15 (other)

HED

v8.4.0

Size

1.37 GB

Version

v1.0.5

Updated

Oct 8, 2026