…out in Cognitive, Affective, & Behavioral Neuroscience. Your best bet for understanding this…
…EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10…
…EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10…
EEG recordings from children performing letter discrimination and visual search tasks involving the mirror-image letters b and d, collected in an embodied learning for literacy study. Each participant completed a pre-intervention discrimination task, an intervention-phase visual search task, and a post-intervention discrimination task. Participants were assigned to an intervention or control group, and events mark stimulus presentation of b and d letters, including practice trials.
Stereo-EEG recordings from six patients with medically refractory epilepsy performing a scene recognition memory task with concurrent EyeLink eye tracking. The dataset accompanies a study of how the human hippocampus rapidly coordinates effective learning, and is a lossless iEEG-BIDS re-packaging of the authors' public Zenodo release. It includes electrode MNI coordinates, behavioural arrays, eye-tracking files and the authors' analysis code in sourcedata.
This dataset contains EEG recordings acquired with a BioSemi system in an implicit learning paradigm. It is organized according to the EEG-BIDS specification and was prepared with MNE-BIDS. The data are shared as a raw BIDS dataset under a CC0 license.
…Yahya Shirazi, SCCN (Swartz Center for Computational Neuroscience), INC (Institute for Neural…
An EEG dataset from the NeuroTechs group, collected in the context of STEM (science, technology, engineering and mathematics) skills. Recordings were acquired with a Unicorn wearable EEG system through its Python API, and values are provided in microvolts. The dataset is organized according to BIDS with HED annotations.
…Researcher shall use the Dataset only for non-commercial research and educational…
EEG recordings from 13 healthy participants performing a copy-spelling task with a visual 6x7 matrix ERP speller. The speller was designed for learning from label proportions (LLP), an unsupervised, calibrationless classification approach for ERP-based brain-computer interfaces. Two stimulus sequences with different target/non-target ratios allowed LLP to reconstruct mean target and non-target responses without labeled data. In the original online study, 84.5% of characters were spelled correctly on average without prior calibration.