nm000150
The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 1
BEH
…This experiment was **intentional** learning because the subjects knew they would be…
- Participants
- 27
- Size
- 881 MB
- Version
- v1.0.0
- Updated
- Jun 17, 2026
Discover datasets
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- nm000268
Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification
EEG Zarr…Department of Computer Science, Machine Learning Group Address: 10587 Berlin, Germany Country…
- Participants
- 29
- Channels
- 30 (10-05)
- Citations
- 95
- HED
- v8.4.0
- Size
- 6.62 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- nm000272
Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)
EEG Zarr…BCI training, human factors, machine learning on ERP data ## Participants This BIDS…
- Participants
- 22
- Channels
- 8 (10-10)
- HED
- v8.4.0
- Size
- 237 MB
- Version
- v1.0.6
- Updated
- Aug 20, 2026
- nm000172
High-gamma dataset described in Schirrmeister et al. 2017
EEG Zarr…Deep learning with convolutional neural networks for EEG decoding and visualization. Human…
- Participants
- 14
- Channels
- 128 (10-05)
- Citations
- 78
- HED
- v8.4.0
- Size
- 31.3 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- nm000232
THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition
EEG ZarrTHINGS-EEG2 is a large EEG dataset from 10 participants who viewed 16,540 training images and 200 repeatedly presented test images from the THINGS database. Images were shown in rapid serial visual presentation at 5 Hz while 63-channel EEG was recorded at 1000 Hz across four sessions per subject. The dataset supports modeling of human visual object recognition and includes resting-state recordings, the authors' preprocessed epochs, and stimulus images.
- Participants
- 10
- Channels
- 63 (10-10)
- Citations
- 88
- Size
- 241 GB
- Version
- v1.1.0
- Updated
- Jul 5, 2026
- nm000410
Vanderbilt BIEN Lab SOZ Resting State SEEG Classification (processed resting-state SEEG)
iEEG…It accompanies Sundrani et al. (2025), "Deep learning on brief interictal intracranial…
- Participants
- 79
- Size
- 16.5 GB
- Version
- v1.0.0
- Updated
- Oct 8, 2026
- on007162
Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)
EEG ZarrThis 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
- nm000323
Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
EEG Zarr…In training: 'NEURAL NETWORKS AND DEEP LEARNING' (33 characters), in test: 'PATTERN…
- Participants
- 54
- Channels
- 62 (10-05)
- Citations
- 100
- HED
- v8.4.0
- Size
- 132 GB
- Version
- v1.0.5
- Updated
- Oct 8, 2026
- 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
- nm000112
FACED - Finer-grained Affective Computing EEG Dataset
EEG ZarrThe Finer-grained Affective Computing EEG Dataset (FACED) contains 32-channel scalp EEG from 123 healthy participants who watched 28 emotion-eliciting video clips targeting nine emotion categories (four negative, four positive, and neutral). Participants gave self-reported ratings on 12 dimensions, including discrete emotions, valence, arousal, liking, and familiarity. The dataset supports cross-subject affective computing and the development of EEG-based emotion recognition algorithms. This version is converted to BIDS with events annotated by video and emotion labels.
- Participants
- 123
- Channels
- 32 (10-10)
- Citations
- 94
- Size
- 31.4 GB
- Version
- v1.1.3
- Updated
- May 29, 2026