on005872
EEGEyeNet Dataset
EEG Zarr
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- Citations
- 7
- Size
- 43.6 MB
- Version
- v1.0.0
- Updated
- Jun 28, 2026
Discover datasets
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- nm000259
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models
EEG ZarrEEG recordings from 10 healthy participants performing an online P300 row-column speller task with a 6x6 character matrix. The study compared two stimulus types, Famous Faces and Inverting, in combination with language models for online classification. Data were recorded from 32 channels at 256 Hz across two sessions per subject with three runs each, and are annotated with HED tags.
- Participants
- 10
- Channels
- 32 (10-10)
- Citations
- 28
- HED
- v8.4.0
- Size
- 773 MB
- Version
- v1.0.5
- Updated
- Oct 8, 2026
- on007052
PURSUE N400 Word Processing
EEG ZarrEEG dataset from the PURSUE project containing an N400 word processing task, recorded from participants at three primarily undergraduate institutions (Southern California, Massachusetts, and Virginia) in 2017 and 2018. The task design follows the ERP CORE paradigm. The dataset supports event-related potential research on semantic word processing.
- Participants
- 288
- Channels
- 32 (10-10)
- Size
- 8.98 GB
- Version
- v1.0.0
- Updated
- Jun 3, 2026
- on005340
Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech
EEG Zarr…set to the mean f0s used for the speech The code for…
- Participants
- 15
- Channels
- 2
- Citations
- 8
- Size
- 19.1 GB
- Version
- v1.0.0
- Updated
- Jun 26, 2026
- on005574
The "Podcast" ECoG dataset
ANAT iEEG ZarrThe "Podcast" electrocorticography (ECoG) dataset captures intracranial neural activity from 9 participants (1,330 grid, depth, and strip electrodes) who listened to a 30-minute naturalistic story containing over 5,000 words. It includes raw recordings, a minimally preprocessed high-gamma version, the auditory stimuli, an aligned word-level transcript, and linguistic features ranging from acoustic properties to large language model embeddings. Tutorials replicating prior findings are provided to support research on natural language comprehension.
- Participants
- 9
- Size
- 14.4 GB
- Version
- v1.0.0
- Updated
- Jun 27, 2026
- on005059
Paired Associates Learning: Memory for Word Pairs in Cued Recall
BEH iEEG ZarrIntracranial EEG and behavioral event data from a paired associates learning task in which participants studied pairs of visually presented words, solved arithmetic distractor problems, and then performed cued recall. Data were collected from clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania. Recordings include monopolar and bipolar iEEG with subject-specific electrode montages, MNI/Talairach coordinates, and brain region annotations.
- Participants
- 72
- Size
- 167 GB
- Version
- v1.0.0
- Updated
- Jun 26, 2026
- on006374
Expectation effects on repetition suppression in nociception
BEH EEG Zarr[
- Size
- 42.7 GB
- Version
- v1.0.0
- Updated
- Jun 28, 2026
- nm000204
Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)
EEG ZarrEEG recordings from 14 healthy participants performing a visual P300 oddball BCI task for controlling a Bluetooth speaker, one of several home appliances in the original study. The task used a 6-class flash-stimulus paradigm on an LCD display. Data were recorded from 31 channels at 500 Hz, with 50 training and 30 testing blocks per subject. The dataset supports research on ERP-based BCIs for home appliance control and inter-subject transfer learning.
- Participants
- 14
- Channels
- 31 (10-10)
- Citations
- 9
- HED
- v8.4.0
- Size
- 644 MB
- Version
- v1.0.4
- Updated
- Oct 8, 2026
- on004356
Subcortical responses to music and speech are alike while cortical responses diverge
EEG Zarr…https://github.com/maddoxlab/Music_vs_Speech_abr ## Format This dataset is…
- Participants
- 22
- Channels
- 34 (10-10)
- Citations
- 27
- Size
- 213 GB
- Version
- v1.0.0
- Updated
- Jun 24, 2026
- nm000199
Learning from label proportions for a visual matrix speller (ERP)
EEG ZarrEEG 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