Motor Imagery dataset from Cho et al 2017
…Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104…
- Participants
- 52
- Channels
- 64 (10-10)
- Citations
- 91
- HED
- v8.4.0
- Size
- 16.7 GB
- Version
- v1.0.2
- Updated
- Aug 18, 2026
100 results for "scientific data" · page 3 of 10 · ranked by relevance
…Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104…
The Brain, Body, and Behaviour Dataset - Experiment 3 is a multimodal neurophysiological dataset comprising 29 subjects across 2 sessions designed to investigate the effects of attentional state on learning from educational videos. Participants watched six educational videos under two conditions: attentive (with post-video testing) and distracted (with concurrent cognitive load), while simultaneous recordings of brain activity, cardiovascular function, eye movements, and head motion were collected. The dataset includes concurrent EEG, ECG, EOG, gaze tracking, pupil size, and head position data, along with behavioral measures including memory questionnaires and ADHD symptom assessments.
…MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data…
…Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104…
…The complete data are currently being used to answer other scientific questions…
…Original dataset / data paper Please cite the original Scientific Data descriptor when…
This dataset comprises EEG recordings from 84 healthy participants performing motor imagery tasks across multiple paradigms (Graz motor imagery, SSMVEP-MI, and hybrid video/SSVideo paradigms) in two distinct recording environments: a controlled electromagnetically shielded laboratory and a simulated multi-sensory hospital setting. A supplementary dataset from 3 participants recorded in a space station environment is also included, extending the study to microgravity conditions. The dataset is intended to support research on cross-environment robustness, cross-subject decoding, and benchmarking of EEG-based brain-computer interface algorithms.
[ is a multimodal neurophysiological dataset comprising simultaneous recordings of EEG, eye-tracking, cardiac, respiratory, and electrooculographic signals from 43 subjects across two sessions. Participants watched three educational videos (Stim-04, Stim-05, Stim-06) under two attention conditions. In Session 1 (attentive condition), participants viewed the videos and answered comprehension questions afterward. In Session 2 (distracted condition), participants viewed the same three videos in the same order while performing a concurrent backward counting task, with no comprehension testing. This derivative dataset supports investigation of neural and behavioral correlates of attention, learning, and cognitive load during naturalistic video viewing.
A comprehensive EEG database containing electroencephalographic signals from 87 healthy participants performing motor imagery brain-computer interface tasks. The dataset comprises over 20,800 trials (~70 hours of recording) organized into three datasets (A, B, C) using a standardized Graz protocol for right and left hand motor imagery. In addition to raw EEG signals, the database includes detailed participant demographics, personality profiles, cognitive traits, and BCI performance metrics, enabling research on user-performance relationships, cross-user machine learning algorithms, and profile-informed signal classification.