visStim
…Frontiers in Neuroscience. 2024. DOI: [in peer review]. License ------- This dataset is…
- Participants
- 20
- Size
- 8.42 GB
- Version
- v1.0.0
- Updated
- Jun 27, 2026
…Frontiers in Neuroscience. 2024. DOI: [in peer review]. License ------- This dataset is…
…EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10…
…Hämäläinen, MEG and EEG data analysis with MNE-Python, Frontiers in Neuroscience…
…based on recent psychological and neuroscience progress and application needs. Participants provided…
This dataset provides high-density (128-channel) EEG and mouse-tracking data from 31 adults aged 18-33, collected in 2020. It includes resting-state recordings and task recordings from food preference choices and semantic judgments. The resource is intended for studying the dynamic decision processes underlying binary choices in the human brain.
…Million-Trial EEG Dataset with Consumer-Grade Hardware ## Overview Alljoined-1.6M…
This dataset contains simultaneously recorded EEG and fMRI data from 22 adults aged 23 to 51 years, collected at the Nathan Kline Institute while participants watched naturalistic video stimuli. It also includes eye tracking, physiological (respiration) recordings, demographic information, and behavioral measures such as sleep and caffeine intake. The goal is to investigate the relationship between electrical brain activity and hemodynamic fluctuations.
FOODEEG is an open dataset of electroencephalographic (EEG) and behavioural responses to food images from 117 participants. In one session, EEG was recorded during a food categorisation task in which participants judged images as healthy, tasty, or something they were willing to eat. In a second session, participants completed a food go/no-go task, a paired choice task, and questionnaires on dietary style and motivation. The dataset also includes raw and cleaned EEG data and normative ratings of the food stimuli from online samples.
…adquired with a low-cost consumer-grade device. The 10 participants had…
EEG dataset investigating whether episodic future thinking can reduce temporal discounting. Participants described a series of personal future events and were then cued to think about these events while making intertemporal choices. They also rated the vividness of their mental imagery for each event.