on004106
BCIT Advanced Guard Duty
EEG
…The vehicle was periodically subject to lateral perturbing forces, which could be…
- Participants
- 27
- Channels
- 256 (biosemi)
- Citations
- 27
- HED
- v8.0.0
- Size
- 67.6 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
70 results for "Amyotrophic Lateral Sclerosis" · page 7 of 7 · ranked by relevance
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- nm000152
Upper-limb elbow-centered motor imagery dataset (10 classes)
EEG Zarr verifiedThis dataset comprises EEG recordings from 12 healthy participants performing upper-limb motor imagery tasks centered on elbow movements. Participants executed kinesthetic imagery of nine goal-directed tasks (drawer opening, soup preparation, weight lifting, door opening, plate cleaning, combing, pizza cutting, and pick-and-place operations) plus rest, cued by visual stimuli. The dataset contains 330 trials recorded at 1000 Hz using 17 EEG channels and is designed for brain-computer interface (BCI) research and motor imagery classification studies.
- Participants
- 12
- Channels
- 17 (other)
- Citations
- 13
- HED
- v8.4.0
- Size
- 4.18 GB
- Version
- v1.0.3
- Updated
- Aug 18, 2026
- on003474
EEG: Probabilistic Selection and Depression
EEGThis dataset comprises EEG recordings from 121 college-age participants (122 collected, 1 excluded) performing a probabilistic selection task, stratified by depression symptomatology as measured by the Beck Depression Inventory. Collected between 2008-2010 in the lab of John J.B. Allen at the University of Arizona, the data were acquired to investigate neural correlates of decision-making and reward processing in relation to depressive symptoms. Some participants underwent clinical interviews to confirm depression status. NOTE: Data quality considerations include potential mislabeling of HEOG/VEOG channels in some subjects, and some files have had channels interpolated already with no raw data available for reversion. Subject 544 was excluded due to unstable BDI scores between pre-assessment and test session.
- Participants
- 122
- Channels
- 66 (10-10)
- Citations
- 89
- Size
- 16.6 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on006848
AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits
BEH EEGThis dataset comprises raw 64-channel EEG, ECG, PPG, and behavioral data collected from 30 healthy young adults during resting-state and verbal working memory tasks. Participants completed a digit-span serial recall task under four stimulus presentation modes (simultaneous, fast, fast+delay, and slow sequential), enabling investigation of neural and physiological correlates of encoding, maintenance, and retrieval. The dataset supports research on working memory load classification and the relationship between oscillatory brain activity, autonomic signals, and behavioral performance.
- Participants
- 30
- Channels
- 63 (10-10)
- Citations
- 5
- Size
- 41.4 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on003766
A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking
BEH EEGThis dataset combines high-density electroencephalography (128-channel HD-EEG) and mouse-tracking to examine dynamic decision-making processes in the human brain. Collected from 31 adults (ages 18-33), it includes resting-state and task-related EEG data acquired during food preference choices and semantic judgment tasks. The resource provides both raw and preprocessed EEG data with synchronized behavioral measures, enabling investigation of neural correlates underlying binary choice decisions.
- Participants
- 31
- Channels
- 129 (egi-geodesic)
- Citations
- 5
- Size
- 153 GB
- Version
- v1.0.0
- Updated
- Jul 22, 2026
- nm000274
BEETL Competition 2021 Motor Imagery Dataset B — transfer learning benchmark
EEGBeetl2021-B is a preprocessed EEG dataset from the NeurIPS 2021 BEETL competition, focused on transfer learning for motor imagery decoding. It contains 32-channel EEG recordings from 2 healthy subjects performing a 4-class motor imagery task (left hand, right hand, feet, rest) sampled at 200 Hz. The dataset is designed to benchmark transfer learning and domain adaptation algorithms addressing cross-subject and cross-dataset generalization challenges in brain-computer interfaces.
- Participants
- 2
- Channels
- 32 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 1.99 GB
- Version
- v1.0.5
- Updated
- Aug 20, 2026
- on004784
Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts
EEGThis phantom EEG dataset contains recordings from an electrically conductive head model under six controlled conditions: brain-only baseline, and brain signals combined with eye, jaw muscle, neck muscle, motion, or simultaneous artifacts. The dataset includes ground truth brain signals and accompanying EEGLAB scripts for artifact detection validation and parameter optimization, supporting development and benchmarking of EEG artifact removal methods.
- Participants
- 1
- Channels
- 128 (other)
- Citations
- 27
- Size
- 10.8 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000162
BNCI 2025-001 Motor Kinematics Reaching dataset
EEG Zarr verifiedThis dataset comprises preprocessed electroencephalography (EEG) recordings from 20 healthy participants performing a motor imagery task involving discrete reaching movements in four directions (up, down, left, right) with varying speeds and distances. Participants executed 960 trials across 10 blocks while viewing visual cues, with concurrent eye-tracking and motion capture data. The dataset includes extensive preprocessing with artifact correction, source localization, and classification features, making it suitable for brain-computer interface research and motor imagery decoding studies.
- Participants
- 20
- Channels
- 67 (10-05)
- Citations
- 3
- HED
- v8.4.0
- Size
- 15.4 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- nm000220
BEETL Competition 2021 Motor Imagery Dataset A — transfer learning benchmark
EEGBeetl2021-A is a preprocessed motor imagery EEG dataset derived from the BEETL Competition 2021 (NeurIPS Task 2), comprising 63-channel, 500 Hz recordings from healthy subjects performing a four-class motor imagery task (rest, left hand, right hand, feet) during an online BCI racing game (Cybathlon2020IC). The dataset was designed to benchmark transfer learning and domain adaptation methods for subject-independent, cross-dataset EEG-based brain-computer interfacing.
- Participants
- 3
- Channels
- 63 (10-10)
- Citations
- 2
- HED
- v8.4.0
- Size
- 2.17 GB
- Version
- v1.0.6
- Updated
- Aug 20, 2026
- nm000191
BigP3BCI Study F — 6x6 multi-paradigm, 3 sessions (10 healthy subjects)
EEGBigP3BCI Study F is a derivative EEG dataset comprising P300-based brain-computer interface recordings from 10 ALS patients across 3 sessions using a 6x6 character grid speller paradigm. This dataset is extracted from the larger BigP3BCI benchmark (DOI: 10.13026/0byy-ry86) and processed using MOABB 1.5.0. The dataset contains 16-channel EEG data sampled at 256 Hz with visual stimulus presentations classified as target and non-target events, designed for evaluating P300 detection algorithms and BCI speller applications.
- Participants
- 10
- Channels
- 16 (10-10)
- HED
- v8.4.0
- Size
- 1.91 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026