ds005811
NOD-EEG
EEG
- Participants
- 19
Discover datasets
47 results for "hemispheric lateralization" · page 2 of 5 · ranked by relevance
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- nm000156
Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025
EEG- Size
- 2.59 GB
- Updated
- May 25, 2026
- on004022
Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment
EEGImported from OpenNeuro ds004022
- Citations
- 2
- Size
- 635 MB
- Updated
- Jun 1, 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 electroencephalography recordings from 27 stroke patients performing lower-limb motor imagery tasks as part of a longitudinal rehabilitation training protocol. The multi-paradigm design supports research on motor-imagery brain–computer interfaces for gait and lower-limb rehabilitation following stroke, with repeated sessions enabling investigation of learning and neuroplasticity during motor recovery.
- Citations
- 18
- Size
- 5.79 GB
- Updated
- Jun 17, 2026
- on004952
ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding
EEGChineseEEG is a high-density EEG dataset with simultaneous eye-tracking recordings from 10 participants silently reading Chinese novels for approximately 11 hours each. The dataset includes raw and preprocessed EEG data at multiple filtering levels, eye-tracking data, Chinese text materials from two novels (The Little Prince and Garnett Dream), and BERT-based Chinese text embeddings. This resource enables investigation of semantic alignment between neural representations and natural language processing model embeddings during Chinese language comprehension.
- Citations
- 24
- Size
- 696 GB
- Updated
- Jun 26, 2026
- nm000177
Multi-session longitudinal motor imagery EEG dataset (Kumar et al. 2024)
EEGA longitudinal motor imagery EEG dataset from 18 brain-computer interface (BCI)-naive subjects acquired across 6 sessions (1 offline + 5 online) to investigate transfer learning and domain adaptation for calibration-free BCI training. Subjects performed left/right hand motor imagery tasks with visual feedback using 22 EEG channels sampled at 512 Hz. The dataset compares Generic Recentering (unsupervised) and Personally Assisted Recentering (supervised) domain adaptation frameworks, with features extracted as covariance matrices and classified using Riemannian geometry-based methods.
- Size
- 1.66 GB
- Updated
- Jun 23, 2026
- nm000271
Chang et al. 2025 — A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises (Lanzhou Jiaotong University)
EEGA multi-paradigm EEG dataset comprising scalp electroencephalography recordings from 28 participants performing and imagining upper-limb rehabilitation exercises. This dataset is designed to support research on motor imagery and rehabilitation brain-computer interfaces (BCI) for upper-limb motor function recovery and assessment.
- Citations
- 1
- Size
- 3.46 GB
- Updated
- Jun 17, 2026
- nm000160
Multi-joint upper-limb MI dataset from Yi et al. 2025
EEG- Size
- 20.3 GB
- Updated
- May 25, 2026
- ds005810
NOD-MEG
MEG MRI- Participants
- 31
- 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.
- Citations
- 2
- Size
- 8.78 GB
- Updated
- Jun 24, 2026