EEG Pre/Post Intervention Dataset
…used for: - EEG signal analysis - Functional connectivity studies - Pre/post intervention comparisons…
- Participants
- 67
- Channels
- 19 (10-20)
- Size
- 686 MB
- Version
- v1.0.0
- Updated
- Jun 30, 2026
…used for: - EEG signal analysis - Functional connectivity studies - Pre/post intervention comparisons…
Simultaneous EEG and fMRI recordings from healthy volunteers who reported their confidence in the accuracy of decisions they either committed themselves or merely observed. The dataset supports analysis of postdecisional contributions to metacognition and includes raw fMRI, BrainVision EEG, T1 anatomical scans and field maps, along with preprocessed EEG and fMRI derivatives.
HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface research in intracerebral hemorrhage (ICH). It includes 37 participants (17 healthy, 20 ICH patients) who performed two-class left- and right-hand grasping motor imagery over up to three sessions. This MOABB-packaged version contains pre-epoched, preprocessed 32-channel EEG, intended for developing BCI-based rehabilitation approaches.
…Cocktail Party fNIRS Whole-head functional near-infrared spectroscopy (fNIRS) recordings from…
A multi-modal face processing dataset combining simultaneously recorded MEG and EEG with structural MRI from 18 participants. Participants viewed famous, unfamiliar, and scrambled faces and made a symmetry judgment by button press. The data are a curated repackaging of the MEEG portion of ds000117, with reorganized event logs, Hierarchical Event Descriptor (HED) annotations, and EEGLAB-format EEG.
…brain oxygenation was monitored using functional near-infrared spectroscopy to cover key…
Alljoined-1.6M is a large-scale EEG dataset of neural responses to rapid serial visual presentation of natural images from the THINGS database. It was recorded with a consumer-grade 32-channel EMOTIV FLEX2 system in 20 adult participants over four sessions each, yielding more than 1.6 million trials. The dataset was built to test whether deep-learning-based brain-computer interface and semantic decoding research can be done with affordable EEG hardware.
[ used to investigate BCI illiteracy and variation in decoding performance across subjects and sessions.
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005935-blue…