on007175
FFR-active-listening
EEG
Imported from OpenNeuro ds007175
- Participants
- 1
- Channels
- 64 (10-10)
- Size
- 226 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "speech decoding" · page 10 of 10 · ranked by relevance
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- on005872
EEGEyeNet Dataset
EEG[
- Citations
- 7
- Size
- 43.6 MB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on007763
BCCWJ-MEG
ANAT MEGThis dataset comprises magnetoencephalography (MEG) recordings from 35 Japanese native speakers who read Japanese newspaper articles word by word, presented via rapid serial visual presentation. It forms part of the BCCWJ-Brain collection, which includes fMRI, MEG, and EEG data from separate participant groups exposed to the same stimuli, enabling cross-modal comparisons of language processing at high spatial and temporal resolution. T1-weighted structural images were also acquired for source localization purposes.
- Participants
- 35
- Citations
- 74
- Size
- 166 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
- nm000235
Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025
EEGA multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery and motor execution tasks. The dataset comprises 2,520 trials across 63 sessions with 64-channel EEG recordings at 1000 Hz sampling rate, focusing on left and right hand grasping imagery. This resource supports the development and benchmarking of brain-computer interface (BCI) systems for motor control applications.
- Participants
- 31
- Citations
- 11
- HED
- v8.4.0
- Size
- 4.65 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000180
Brennan2019: EEG during Naturalistic Listening to Alice in Wonderland
EEG…Task ---- - **Task label:** `alicelistening` - Passive listening to continuous naturalistic speech (an audiobook…
- Participants
- 45
- Channels
- 60 (other)
- Citations
- 127
- Size
- 3.81 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on004521
Changes in behavioral priority influence the accessibility of working memory content - Experiment 1
EEG[
- Size
- 10.7 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000274
BEETL Competition 2021 Motor Imagery Dataset B — transfer learning benchmark
EEG…transfer learning for motor imagery decoding. The dataset contains 32-channel EEG…
- Participants
- 2
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 1.99 GB
- Version
- v1.0.5
- Updated
- Aug 20, 2026
- on005795
MULTI-CLARID (Multimodal Category Learning and Resting-state Imaging Data)
ANAT EEG FUNCThis dataset presents multimodal simultaneous fMRI/EEG data collected from participants performing an auditory category learning task, alongside resting-state fMRI scans. Each dataset includes 63-channel EEG with additional ECG, EOG, facial EMG, and skin conductance recordings, a physiological file capturing respiration and finger-pulse signals, and structural MRI (T1-weighted and PD-weighted UTE) scans for anatomical reference and electrode localization. Collected at the Combinatorial NeuroImaging core facility of the Leibniz Institute for Neurobiology, the data support research into multi-dimensional auditory category learning and its neural correlates.
- Participants
- 34
- Channels
- 72 (10-10)
- Citations
- 9
- Size
- 6.43 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000345
CastillosBurstVEP40
EEG…EEG2Code bitwise decoding Cross-Validation ---------------- Evaluation type: offline Performance (Original Study) ---------------------------- Accuracy…
- Participants
- 12
- Channels
- 32 (10-10)
- Citations
- 21
- HED
- v8.4.0
- Size
- 337 MB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- nm000142
Ear-EEG motor execution dataset from Wu et al 2020
EEG Zarr verifiedThis dataset contains simultaneous scalp and ear-EEG recordings from 6 healthy right-handed adults performing a motor execution task involving left and right hand fist clenching, cued by visual arrow stimuli. Recordings were made at 1000 Hz using a 122-channel Neuroscan SynAmps2 system, yielding 1114 trials, and are intended for benchmarking motor task classification algorithms such as EEGNet. The dataset was converted to BIDS format using MOABB and originates from a study investigating the utility of in-ear EEG sensing for motor task classification.
- Participants
- 6
- Channels
- 122 (10-05)
- Citations
- 12
- HED
- v8.4.0
- Size
- 18.4 GB
- Version
- v1.0.3
- Updated
- Aug 20, 2026