Discover datasets

# Different Doors Forty participants selected one of two doors, then received feedback indicating either a monetary gain or loss. Blocks lasted 20 trials and, unbeknownst to participants, were of two types. In learnable blocks, one of the doors was better because choosing it was associated with a 60% likelihood of a win. The other door only paid out 10% of the time. In unlearnable blocks, outcomes were not yoked to participant actions but were instead drawn from the learnable blocks and present

Participants

40

Size

3.34 GB

Participants

2

Size

1.87 GB

Version

v1.0.0

Updated

May 4, 2026

Participants

85

Size

126 GB

Version

v1.0.0

Updated

Apr 29, 2026

Participants

10

Size

159 GB

Version

v1.0.1

Updated

May 11, 2026

# No effect of rhythmic visual stimulation on experimental pain perception This dataset contains EEG recordings from 41 participants, collected as part of a study investigating the effect of rhythmic visual stimulation on experimental pain perception. Data were collected at Université Laval, Québec, Canada. This work was funded by the Fonds de recherche du Québec – Santé (FRQS) and the Quebec Pain Research Network (QPRN). For full details on the dataset and study design, please refer to the r

Participants

41

Size

24.3 GB

# Dataset of Emotion Recognition Using Validated Video Stimuli with Large-scale Behavioral Survey and MEG Recordings ## General Description This dataset was developed as part of research focused on brain signal-based emotion recognition by capturing high-fidelity Magnetoencephalography (MEG) signals during induced different emotional states. The dataset is organized into three primary components: 1. **Phenotype (Online Survey)**: Stored within the 'phenotype' directory, this component contain

Participants

23

Size

88.8 GB

64-ch EEG, 95 subjects, 2 sessions, 6 paradigms (13 tasks). BrainAmp 250Hz Easycap 64-ch. DOI:10.1016/j.neuroimage.2022.119666

Version

v1.0.0

Updated

May 11, 2026

# ROAMM: Reading Observed At Mindless Moments **ROAMM** is a large-scale multimodal dataset featuring simultaneous **EEG and eye-tracking** data collected during naturalistic reading with **span-level mind-wandering annotations**. ROAMM provides a benchmark dataset for MW detection and EEG-to-text decoding tasks, and enables the study of attention-related degradation in language decoding from brain activity in naturalistic reading. ## Dataset Status * **Raw EEG (BIDS):** **Work in Progress.**

Participants

1

Size

224 MB

# EEG-Speech Brain Decoding Dataset ## Overview This dataset contains EEG recordings and audio data. ## Sessions Sessions are labeled by recording date in YYYYMMDD format. - Example: `ses-20240401` = recorded on April 1, 2024 Multiple recordings on the same day are distinguished by run numbers: - `run-N`: Nth recording of the day ## Tasks - **speechopen**: Overt speech production task - Participants vocalize visually presented text - **listening**: Auditory listening task - Participants

Participants

3

Size

955 GB

# Cross-Environment Multi-Paradigm Motor Imagery EEG Dataset # Description This dataset contains EEG recordings collected during motor imagery (MI) tasks under different recording environments. The primary dataset includes recordings from a controlled laboratory setting and a simulated hospital environment, designed to investigate the impact of environmental variability on EEG-based brain–computer interface (BCI) performance.In addition, a small supplementary dataset recorded in a space station

Participants

84

Size

125 GB