Different Doors
…learnable blocks and presented in random order. Thus, the win and loss…
- Participants
- 40
- Channels
- 32 (10-10)
- Citations
- 1
- Size
- 3.34 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
100 results for "random-dot kinematogram" · page 7 of 10 · ranked by relevance
…learnable blocks and presented in random order. Thus, the win and loss…
NEMAR Dataset nm000106: handwriting - Handwriting movement detection from EMG
…8 words were presented in random order, each repeated 40 times. - EEG…
A multicenter intracranial electroencephalography (iEEG) dataset comprising segmented 3-second single-channel clips from epilepsy patients, annotated for graphoelement classification and artifact detection. The dataset includes 10,000+ clips from multiple subjects with preserved clinical metadata including seizure onset zone (SOZ) flags, electrode anatomy, and reviewer annotations. This resource supports the development and validation of automated signal processing algorithms for clinical neurophysiology applications.
…The order of the six tasks was randomized across participants, and the…
A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants performing left and right hand motor imagery in response to visual cues. Recorded at 1000 Hz using a 64-channel EEG montage with standardized electrode placement, this dataset supports brain-computer interface research and analysis of ocular activity patterns during motor imagery paradigms.
…multi-user/hyperscanning experiment with three randomized conditions (Solo1, Solo2, Collaboration). Subjects…
Beetl2021-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.
…adding predictive and non-predictive (random) pre-perturbation onset audio cues and…
Imported from OpenNeuro ds004745