on005594
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EEG Zarr
[
- Citations
- 2
- Size
- 10.9 GB
- Version
- v1.0.0
- Updated
- Jun 27, 2026
Discover datasets
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- nm000224
Imagined speech EEG dataset — short and long words (Nguyen et al. 2017)
EEG ZarrPreprocessed 64-channel EEG recordings from six healthy participants performing imagined speech of a short word ('in') versus a long word ('cooperate'). The data were band-pass and notch filtered and cleaned of EOG artifacts. They were used to classify imagined speech with Riemannian manifold covariance features and relevance vector machines for brain-computer interface applications. The dataset is re-hosted in BIDS format through the MOABB benchmark collection.
- Participants
- 6
- Channels
- 60 (10-10)
- Citations
- 99
- HED
- v8.4.0
- Size
- 1.15 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on005558
Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier)
BEH iEEG Zarr…The word lists in this paradigm follow a specific semantic construction. Each…
- Participants
- 9
- Size
- 12.2 GB
- Version
- v1.0.0
- Updated
- Jun 27, 2026
- on007629
ROAMM
BEH EEG ZarrROAMM (Reading Observed At Mindless Moments) is a multimodal dataset of simultaneous EEG and eye-tracking recordings from 44 participants during naturalistic reading, with span-level mind-wandering annotations collected through a retrospective self-report paradigm. It serves as a benchmark for mind-wandering detection and EEG-to-text decoding, and supports research on how attention lapses degrade language decoding from brain activity. Synchronized machine-learning-ready data, reading stimuli and page-level comprehension scores are included.
- Participants
- 1
- Channels
- 64 (10-10)
- Size
- 46.6 GB
- Version
- v1.0.0
- Updated
- Jun 30, 2026
- on005089
Proactive selective attention across competition contexts
EEG ZarrEEG dataset investigating proactive selective attention across different competition contexts. The dataset is mirrored on NEMAR from OpenNeuro (ds005089) and organized in BIDS format.
- Participants
- 36
- Channels
- 63 (10-10)
- Citations
- 6
- Size
- 68.0 GB
- Version
- v1.0.0
- Updated
- Jun 26, 2026
- on006817
Visual Attribute-Specific Contextual Trajectory Paradigm 2.0
EEG ZarrThis dataset contains raw neuroimaging recordings collected with the Visual Attribute-Specific Contextual Trajectory Paradigm 2.0. The paradigm is used to study how the brain generates domain-general visual surprise signals and how their latency depends on the visual attribute that is violated. It accompanies a pre-print on attribute-dependent latency of visual surprise signals.
- Participants
- 35
- Citations
- 1
- Size
- 11.4 GB
- Version
- v1.0.0
- Updated
- Jun 29, 2026
- on006850
Urban Appraisal: Physiological Recording during Rating of Different Urban Environments
EEG Zarr…with 9 different prompts & scales. Semantic segmentation was used to extract area…
- Participants
- 63
- Channels
- 66 (other)
- Size
- 34.7 GB
- Version
- v1.0.0
- Updated
- Jun 29, 2026
- nm000402
Multi-Expert Seizure Annotation
iEEGStereotactic EEG recordings of 78 seizures (44 spontaneous, 34 stimulation-induced) from 29 patients with drug-resistant epilepsy, recruited at the University of Pennsylvania and the Children's Hospital of Philadelphia. Three or five board-certified epileptologists independently annotated each seizure for onset time, onset channels, and channels seizing 10 seconds after onset. The BIDS-formatted data include electrode localizations, patient demographics, and clinical outcomes. They support benchmarking of automated seizure onset and spread detection against expert performance and comparison of spontaneous and stimulation-induced seizure networks.
- Participants
- 29
- Size
- 7.95 GB
- Version
- v1.0.0
- Updated
- Oct 8, 2026
- on005565
Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers
EEG Zarr[
- Size
- 2.62 GB
- Version
- v1.0.0
- Updated
- Jun 27, 2026
- nm000228
Nieuwland et al. 2018: Multi-site N400 Replication Study
EEG ZarrA large-scale (N=356) multi-laboratory EEG replication of DeLong, Urbach & Kutas (2005), testing whether readers pre-activate the phonological form of upcoming nouns during sentence comprehension. Participants at nine UK laboratories read sentences word by word, with indefinite articles (a/an) preceding expected or unexpected nouns. The data show reliable N400 effects on target nouns but no statistically significant effect on the preceding articles, which challenges strong accounts of prediction. The release includes all subjects with available raw data and a flag marking those included in the original paper's analyses.
- Participants
- 356
- Channels
- 63 (10-10)
- Citations
- 98
- Size
- 103 GB
- Version
- v1.1.0
- Updated
- Jul 5, 2026