Discover datasets

This dataset contains 62-channel EEG recorded at 500 Hz from 15 healthy participants across two sessions. It combines resting-state recordings with three difficulty levels (easy, medium, difficult) of the MATB-II multitasking paradigm, and was designed for passive brain-computer interface research on cross-session mental workload estimation. The BIDS version is a MOABB-derived conversion of the COG-BCI database, with HED event annotations.

Participants

15

Channels

61 (10-10)

Citations

70

HED

v8.4.0

Size

1.22 GB

Version

v1.0.0

Updated

Jun 2, 2026

High-density EEG (256 channels) recorded from 23 healthy right-handed volunteers in Rennes, France, during visual object naming and spelling tasks. Each participant completed two conditions with two runs each, with 74 picture stimuli per run balanced for animals/objects and long/short words. The dataset supports studies of functional brain network dynamics during language and picture-naming tasks.

Participants

23

Channels

256 (egi-geodesic)

Size

90.4 GB

Version

v1.0.0

Updated

Jun 21, 2026

EEG recordings from fifteen healthy participants who executed and imagined six sustained upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) plus a rest condition, in two separate sessions. The dataset supports decoding of single upper limb movements from the time-domain of low-frequency EEG. It is relevant to non-invasive control of motor neuroprostheses and robotic arms. This BIDS version was generated from the original BNCI Horizon 2020 data using MOABB.

Participants

15

Channels

61 (10-05)

Citations

93

HED

v8.4.0

Size

34.0 GB

Version

v1.0.3

Updated

Aug 18, 2026

A single-session EEG dataset of left- versus right-hand motor imagery recorded from 55 healthy, BCI-naive participants using a 16-channel g.Nautilus system at 250 Hz. Each participant completed one calibration run without feedback and three feedback runs, for 160 trials in total. The data were collected to examine psychological and cognitive predictors of motor-imagery brain-computer interface performance. This BIDS release was converted from the original DataverseNL deposit via MOABB.

Participants

55

Channels

16 (10-10)

HED

v8.4.0

Size

5.30 GB

Version

v1.0.0

Updated

Oct 8, 2026

EEG dataset from 19 participants performing a visual P300 speller-like paradigm to assess how visual stimulus properties affect evoked responses and classification accuracy. Each participant completed three sessions with conditions varying the number of stimuli (4 to 9) and the flash type (standard flash versus a superimposed yellow smiling cartoon face). Recordings comprise 382 files from eight electrodes sampled at 256 Hz, with markers encoding trial phases and activated symbols.

Participants

19

Channels

9 (10-10)

Citations

1

Size

1.27 GB

Version

v1.0.0

Updated

May 13, 2026

This dataset contains EEG recordings from 34 participants (one session each) who viewed 242 object images, 121 challenge and 121 control images, selected from comparisons between human behavioural performance and AlexNet. The study examines adaptive recruitment of cortex-wide recurrent processing during visual object recognition. The derivatives folder contains outputs of the decoding analyses, including time-resolved decoding accuracy matrices for object identity (.npz).

Participants

34

Channels

63 (10-10)

Citations

2

Size

61.2 GB

Version

v1.0.0

Updated

Jun 30, 2026

Electrophysiological dataset recorded from the amygdalae of nine epilepsy patients undergoing preoperative invasive monitoring in the mesial temporal lobe. Participants viewed dynamic visual sequences of fearful faces (aversive condition) interleaved with neutral landscapes (neutral condition). The dataset provides intracranial EEG in BIDS format, together with neuronal spike times and waveforms in NIX format, enabling study of amygdalar responses to aversive stimuli from macroscopic field potentials to single-neuron firing.

Participants

9

Citations

24

Size

297 MB

Version

v1.0.0

Updated

Jun 21, 2026

This dataset contains EEG recordings acquired with a BioSemi system in an implicit learning paradigm. It is organized according to the EEG-BIDS specification and was prepared with MNE-BIDS. The data are shared as a raw BIDS dataset under a CC0 license.

Participants

61

Size

114 GB

Version

v1.0.0

Updated

Jun 20, 2026