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on003825
Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts

EEG Zarr

Electroencephalography recordings from 50 participants viewing 22,248 images spanning 1,854 object concepts from the THINGS database. Images were shown in rapid serial visual presentation streams at 10 Hz while participants performed an orthogonal fixation colour change detection task. The dataset supports research on the temporal dynamics of object recognition and visual representation in the human brain.

Participants

50

Channels

63

Citations

3

Size

55.3 GB

Version

v1.0.0

Updated

Mar 14, 2026

by Grootswagers et al. OpenNeuro ds003825
on005558
Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier)

BEH iEEG Zarr

…feature matrix, and the label vector is the recalled status of the…

Participants

9

Size

12.2 GB

Version

v1.0.0

Updated

Jun 27, 2026

by Haydn G. Herrema et al. OpenNeuro ds005558
on007964
Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding

EEG Zarr

Raw 128-channel EEG data from 20 observers viewing object images in a rapid presentation paradigm, together with representational dissimilarity matrices. The dataset supports time-resolved neural decoding analyses that separate objects' contextual associations from their perceptual and conceptual attributes. One participant was excluded because of a technical recording error, leaving 19 usable participants.

Participants

19

Citations

2

Size

18.9 GB

Version

v1.0.0

Updated

Jun 17, 2026

by Kim et al. OpenNeuro ds007964
nm000360
Annotated interictal epileptiform discharges in intracranial EEG (iEEG) sleep data (Falach et al., 2024)

iEEG

…sleep onset and sleep-stage vectors. They contain no names, dates of…

Participants

25

Size

603 MB

Version

v1.0.0

Updated

Oct 8, 2026

by Rotem Falach et al.
nm000183
Multicenter iEEG dataset for classification of graphoelements and artifactual signals (FNUSA)

iEEG Zarr

This dataset contains independent 3-second single-channel intracranial EEG (stereo-EEG) clips from St. Anne's University Hospital (FNUSA), annotated for classification of physiological and pathological graphoelements and artifactual signals. It is a BIDS conversion of the segmented .mat archives released with Nejedly et al. 2020. Clips from one subject and one SEEG contact are concatenated per run at 5000 Hz, with events preserving labels, anatomy, reviewer, seizure onset zone flag, and electrode type.

Participants

14

Size

10.8 GB

Version

v1.0.2

Updated

Jun 29, 2026

by Petr Nejedly et al.
nm000390
Brain criticality predicts individual levels of inter-areal synchronization (Fusca, Siebenhuehner, Wang et al., 2023): SEEG/MEG PLV and DFA (derivative)

iEEG

…3.7 ± 4 per subject), Vectorview/Triux 306-channel, BioMag Laboratory, Helsinki…

Participants

68

Size

3.78 GB

Version

v1.0.0

Updated

Oct 8, 2026

by Felix Siebenhühner et al.
on003848
Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG

ANAT iEEG Zarr

A clinical intracranial EEG (iEEG) dataset from 12 epilepsy surgery patients drawn from the RESPect (Registry for Epilepsy Surgery Patients) collection at the University Medical Center Utrecht. It includes six intraoperative ECoG recordings and six long-term recordings (three ECoG, three SEEG). The data are organized in BIDS, with session and run conventions that capture the surgical stage or the monitoring day and time, and the task labels describe the patient's state.

Participants

6

Citations

1

Size

99.3 GB

Version

v1.0.0

Updated

Jun 22, 2026

by van Blooijs D. et al. OpenNeuro ds003848
nm000389
Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings (Siebenhuehner et al., 2020): SEEG and MEG connectomes (derivative)

iEEG MEG

…2 contributed 3), 306-channel Vectorview/Triux (Elekta-Neuromag/MEGIN), BioMag Laboratory…

Participants

84

Size

13.1 GB

Version

v1.0.0

Updated

Oct 8, 2026

by Felix Siebenhühner et al.
nm000199
Learning from label proportions for a visual matrix speller (ERP)

EEG Zarr

EEG recordings from 13 healthy participants performing a copy-spelling task with a visual 6x7 matrix ERP speller. The speller was designed for learning from label proportions (LLP), an unsupervised, calibrationless classification approach for ERP-based brain-computer interfaces. Two stimulus sequences with different target/non-target ratios allowed LLP to reconstruct mean target and non-target responses without labeled data. In the original online study, 84.5% of characters were spelled correctly on average without prior calibration.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.3

Updated

Oct 8, 2026

by David Hübner et al.
on002336
A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1

ANAT EEG FUNC Zarr

…eeg signal in C2) → .nf (vector of NF scores -4 per s…

Participants

10

Size

18.0 GB

Version

v1.0.0

Updated

Jun 21, 2026

by Giulia Lioi et al. OpenNeuro ds002336