Executive Functionning Study for Assessing the Effect of Neurofeedback
- Participants
- 24
- Channels
- 64 (10-10)
- Citations
- 50
- Size
- 26.4 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
100 results for "recurrent processing" · page 6 of 10 · ranked by relevance
[ dataset is a human electroencephalography resource comprising high-resolution EEG recordings from 20 participants performing multisensory perception and mental imagery tasks. Signals were acquired at 1000 Hz sampling rate during exposure to unimodal (visual and auditory) and multimodal stimuli, with participants providing subjective vividness ratings. Technical validation through event-related potentials and power spectral density analyses confirmed distinct neural responses across stimulus conditions, supporting applications in neural decoding, perception, and cognitive modeling.
…supported project R01 MH122258 "CRCNS: Processing speed in the human connectome across…
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005273-blue…
This dataset contains intracranial EEG recordings and behavioral event data from patients performing a delayed free recall task, in which participants studied word lists, engaged in an arithmetic distractor task, and freely recalled words. Data were collected across multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania, providing electrode localization and referencing information suitable for memory and epilepsy research.
…False ## Signal Processing - **Classifiers**: LDA, SVM, Random Forest, kNN, Naive Bayes, CCA…
This dataset contains behavioral events and intracranial EEG recordings from a categorized free recall task with open-loop electrical stimulation applied during encoding. Participants studied semantically organized word lists, performed arithmetic distractor tasks, and then freely recalled the words. Stimulation was delivered to single electrodes in the hippocampus or entorhinal cortex during word encoding on a subset of trials, with data collected across multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania.
Imported from OpenNeuro ds007176
…False ## Signal Processing - **Classifiers**: LDA, SVM, Random Forest, kNN, Naive Bayes, AdaBoost…