nm000156
Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025
EEG Zarr verified
- Participants
- 37
- Channels
- 32 (10-10)
- HED
- v8.4.0
- Size
- 2.59 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
68 results for "hemispheric lateralization" · page 2 of 7 · ranked by relevance
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- on004022
Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment
EEGImported from OpenNeuro ds004022
- Participants
- 7
- Channels
- 18
- Citations
- 2
- Size
- 635 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on006095
Mind in Motion Older Adults Walking Over Uneven Terrain
EEGThis dataset comprises high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, and ground reaction force recordings collected from older adults walking over uneven terrain at varying speeds. Each participant completed multiple walking trials under different conditions as well as a seated rest trial, with digitized electrode locations provided. The dataset supports research into the neural and biomechanical mechanisms underlying balance and gait control during complex locomotion in aging populations.
- Participants
- 71
- Channels
- 120 (other)
- Citations
- 1
- Size
- 130 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on004952
ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding
EEGChineseEEG is a high-density EEG and simultaneous eye-tracking dataset collected from 10 participants silently reading two Chinese novels over approximately 11 hours. The dataset includes raw and multiple stages of pre-processed EEG data, along with BERT-base-chinese text embeddings of the reading materials, designed to support research on semantic alignment between NLP model representations and neural activity. It provides a resource for studying naturalistic language processing and neural decoding using Chinese linguistic stimuli.
- Participants
- 10
- Channels
- 128 (egi-geodesic)
- Citations
- 24
- Size
- 697 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000177
Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)
EEGThis dataset comprises longitudinal motor imagery EEG recordings from 18 BCI-naive subjects across six sessions (one offline, five online), designed to study transfer learning and skill acquisition in brain-computer interfaces (BCIs). It compares two domain adaptation frameworks—Generic Recentering and Personally Assisted Recentering—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were collected at 512 Hz with 22 EEG channels and analyzed using Riemannian geometry-based classifiers.
- Participants
- 18
- Channels
- 22 (10-10)
- Citations
- 49
- HED
- v8.4.0
- Size
- 14.4 GB
- Version
- v1.0.4
- Updated
- Aug 20, 2026
- nm000271
Chang et al. 2025 — A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises (Lanzhou Jiaotong University)
EEGThis dataset contains scalp EEG recordings from 28 participants performing or imagining upper-limb rehabilitation exercises under a multi-paradigm protocol. It was designed to support research on motor-imagery-based brain–computer interfaces (BCI) for upper-limb rehabilitation. The data have been converted to BIDS format from the original dataset published by Chang et al. (2025).
- Participants
- 28
- Channels
- 59 (10-10)
- Citations
- 3
- HED
- v8.4.0
- Size
- 47.1 GB
- Version
- v1.0.5
- Updated
- Aug 20, 2026
- nm000270
Liu et al. 2025 — Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients (Tianjin University)
EEGThis dataset comprises scalp EEG recordings from 27 stroke patients performing a lower-limb motor imagery task as part of a multi-paradigm, longitudinal rehabilitation training protocol. Data were collected at Tianjin University to support research on motor-imagery brain-computer interfaces for gait and lower-limb rehabilitation after stroke. The dataset is organized in BIDS format with a single EEG task (task-imagery) across repeated sessions.
- Participants
- 27
- Channels
- 64 (10-10)
- Citations
- 23
- HED
- v8.4.0
- Size
- 56.7 GB
- Version
- v1.0.5
- Updated
- Aug 20, 2026
- nm000160
Multi-joint upper-limb MI dataset from Yi et al. 2025
EEG Zarr verifiedA multi-joint upper-limb motor imagery EEG dataset comprising 18 healthy subjects performing eight distinct imagery tasks involving hand, wrist, elbow, and shoulder movements. The dataset contains 320 trials per subject acquired at 1000 Hz using 62-channel EEG with visual cue-based paradigm, designed for brain-computer interface research and motor rehabilitation applications.
- Participants
- 18
- Channels
- 62 (10-10)
- Citations
- 4
- HED
- v8.4.0
- Size
- 50.0 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
- on005863
Cognitive Electrophysiology in Socioeconomic Context in Adulthood
EEGThis dataset contains EEG recordings from 127 young adults (18-30 years) collected alongside measures of childhood and adulthood socioeconomic status (SES), including educational attainment, income, food security, and neighborhood characteristics. EEG tasks were drawn from or adapted from the ERP CORE resource, designed to elicit neural activity related to perception, cognition, and action. The dataset also includes an ADHD symptoms checklist, enabling investigation of relationships between SES, ADHD symptoms, and neural activity in a socioeconomically diverse adult sample.
- Participants
- 127
- Channels
- 27
- Citations
- 7
- Size
- 10.6 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on004554
Forced Picture Naming Task
EEGThis dataset comprises preprocessed EEG recordings from 16 native French-speaking participants performing a forced picture naming task. Participants viewed images from the Snodgrass & Vanderwart corpus and were required to name them while EEG activity was recorded across baseline, visual stimulation, and naming phases. The dataset contains 270 trials per subject and was used to characterize spatiotemporal dynamics in EEG data using optical flow pattern analysis. Note: 16 participants represent the final cohort after 4 exclusions from an initial 20 subjects due to hardware failure.
- Participants
- 16
- Channels
- 99 (10-05)
- Citations
- 3
- Size
- 8.79 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026