on007052
PURSUE N400 Word Processing
EEG
[
- Size
- 8.98 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
Discover datasets
100 results for "Semantic Annotation" · page 4 of 10 · ranked by relevance
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- on007964
Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding
EEGThis dataset contains 128-channel EEG recordings from 20 observers (19 included in final analysis) viewing object images at 3.33 Hz to investigate how contextual associations, perceptual attributes, and conceptual properties of objects are represented in neural activity. One participant was excluded due to a technical error in EEG recording. Time-resolved neural decoding was applied to disentangle these distinct representational dimensions from the EEG signals.
- Participants
- 20
- Citations
- 2
- Size
- 18.9 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on005406
EEG frequency tagging reveals the integration of dissimilar observed actions
EEG[
- Size
- 13.3 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000263
Visual object ERP EEG dataset (Kaneshiro et al. 2015)
EEG Zarr fidelity issueA 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
- nm000278
ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading
EEG…reading Wikipedia sentences while annotating specific semantic relations.) Each reading block is…
- Participants
- 12
- Channels
- 128 (egi-geodesic)
- Citations
- 95
- Size
- 19.3 GB
- Version
- v1.0.0
- Updated
- Sep 16, 2026
- on007655
MorseEEG-ATP
BEH EEG…Trial-aligned event annotations, including event onsets, trigger values, block labels, speed…
- Participants
- 32
- Channels
- 62 (10-10)
- Size
- 14.6 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
- on006159
Implicit Learning EEG (BioSemi)
BEH EEGAn electroencephalography (EEG) dataset acquired using BioSemi equipment to investigate implicit learning processes. This raw neuroimaging dataset contains EEG recordings organized according to the Brain Imaging Data Structure (BIDS) standard, facilitating standardized analysis and sharing of electrophysiological data.
- Participants
- 61
- Size
- 114 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000224
Imagined speech EEG dataset — short and long words (Nguyen et al. 2017)
EEG Zarr verifiedThis dataset comprises preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks, specifically discriminating between short and long words ('cooperate' vs 'in'). Data were acquired at 256 Hz using 64 EEG channels and processed with bandpass filtering (8-70 Hz), notch filtering (60 Hz), and artifact removal. The dataset contains 1,200 trials. Classification results (mean accuracy 73.3±8.9%) reported in the original Nguyen et al. 2017 study are provided for reference.
- Participants
- 6
- Channels
- 60 (10-10)
- Citations
- 98
- HED
- v8.4.0
- Size
- 1.15 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on004196
Bimodal dataset on Inner speech
ANAT EEG FMAP FUNC[
- Citations
- 27
- Size
- 9.33 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000252
Imagined speech EEG dataset — long words condition (Nguyen et al. 2017)
EEG Zarr verifiedThis dataset comprises EEG recordings from 6 healthy participants performing imagined speech tasks involving two conditions: cooperate and independent word imagery. The study employed a motor imagery paradigm with auditory and visual cues, yielding 3,600 preprocessed trials (1,800 per class) recorded at 256 Hz from 64 channels. Data were analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications, achieving mean classification accuracy of 66.2±4.8%.
- Participants
- 6
- Channels
- 60 (10-10)
- Citations
- 97
- HED
- v8.4.0
- Size
- 1.27 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026