100 results for "multimodal neuroimaging" · page 6 of 10 · ranked by relevance
[. The dataset includes 59 EEG channels sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials (51 subjects × 3 sessions × 200 trials for 2-class + 11 subjects × 3 sessions × 300 trials for 3-class) with visual and auditory cues. This resource is designed to support the development and benchmarking of motor imagery BCI algorithms and classifiers.
Beetl2021-A is a preprocessed motor imagery EEG dataset from the BEETL Competition Task 2 (NeurIPS 2021), comprising data from 3 healthy subjects collected during an online racing game (Cybathlon2020IC). The dataset contains 63-channel EEG recordings at 500 Hz with four-class motor imagery tasks (rest, left hand, right hand, feet) and serves as a benchmark for evaluating transfer learning and domain adaptation methods across heterogeneous EEG datasets and subjects. This dataset is part of a larger competition focused on advancing transfer learning for subject independence and cross-dataset generalization in brain-computer interfacing.
2417 clinical EEG recordings, 19-ch 10-20 montage, normal/abnormal labels. EDF format. Zenodo DOI:10.5281/zenodo.10909103
…This experiment aims to address these gaps by creating a comprehensive multimodal…
Imported from OpenNeuro ds003380
Beetl2021-B is a preprocessed 32-channel EEG dataset from the NeurIPS 2021 BEETL competition designed for benchmarking transfer learning and domain adaptation algorithms in motor imagery brain-computer interfaces. The dataset comprises EEG recordings from 2 healthy subjects performing 4-class motor imagery tasks (left hand, right hand, feet, and rest) sampled at 200 Hz, with 1590 total trials organized for cross-subject and cross-dataset generalization assessment.
…Preprocessing was carried out with fMRIPrep (v20.2.0), following standard neuroimaging…