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Experiment Details We recorded EEG from 24 pairs of participants while they performed a 4-way categorisation task based on rules they first agreed upon together. In addition, participants did a pre- and post-test on the same stimuli. Experiment length: 1 hour More information: Pre-print: Moerel, D., Grootswagers, T., Quek, G.L., Smit, S., & Varlet, M. (2025). Information alignment between interacting brains. bioRxiv. doi: https://doi.org/10.1101/2025.01.07.631802 Code: https://doi.org/10.1

Participants

24

Size

62.2 GB

Updated

Jun 6, 2026

The RECAP-EEG: Retrieval with Feedback and Cognitive Adaptation EEG Dataset provides an open-access collection of human electroencephalography (EEG) recordings aimed at investigating the neural correlates of learning processes in educational contexts. The study involved 21 neurotypical undergraduate students (mean age = 23.10 years, SD = 3.92) and was conducted at the Federal University of Paraíba (UFPB), Brazil. Participants were randomly assigned to one of three experimental groups through an

Participants

21

Size

1.32 GB

Updated

Jun 6, 2026

Subjects (N = 30) completed a Multiple Object Monitoring (MOM) task. Methodological details can be read within the pre-print: https://doi.org/10.1101/2025.07.10.663816 Please email [email protected] if you have any further questions.

Participants

30

Size

6.52 GB

Updated

Jun 6, 2026

Experiment Details Participants played a computerised version of the competitive Rock-Paper-Scissors game (480 games). We recorded 64 channel EEG from 62 participants, grouped into 31 pairs. Experiment length: 1 hour More information: https://doi.org/10.17605/OSF.IO/YJXKN (OSF repository with more information and analysis code) Moerel, D., Grootswagers, T., Chin, J. L., Ciardo, F., Nijhuis, P., Quek, G. L., Smit, S. & Varlet, M. (2025). Neural decoding of competitive decision-making in Roc

Participants

31

Size

78.0 GB

Updated

Jun 6, 2026

# Details related to access to the data Please contact the following authors for further information: Tong Shan (email: [email protected]) Ross K. Maddox (email: [email protected]) # Overview This study examines pitch-time interactions in music processing by introducing “chimeric music,” which pairs two distinct melodies, and exchanges their pitch contours and note onset-times to create two new melodies, thereby distorting musical pattern while maintaining the marginal statis

Participants

27

Size

193 GB

Updated

Jun 6, 2026

# UCSD Forehead Patch Sleep Validation Dataset Curated EEG recordings for validating sleep staging from a three-electrode forehead patch against standard 33-channel polysomnography (not included in this release -- this release only includes the three-electrode data). ## What is included - CGX patch `.set` files that contain `EEG.VisualHypnogram` and `EEG.SpectralScoring` ## Sleep stage labels `EEG.VisualHypnogram` is manual scoring in 30-second epochs using the following integers 1 equals Wa

Participants

19

Size

9.36 GB

Updated

Jun 6, 2026

Understanding how the brain engages with poetic language is key to advancing empirical research on aesthetic and creative cognition. This experiment involved 64-channel EEG recordings and behavioural ratings from 51 participants who read and evaluated 210 short English-language texts — 70 Haiku (nature-themed), 70 Senryu (emotion-themed), and 70 non-poetic Control texts. Each poem/text was rated on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativ

Participants

47

Size

45.4 GB

Updated

Jun 6, 2026

Understanding how the brain engages with poetic language is key to advancing empirical research on aesthetic and creative cognition. This experiment involved 64-channel EEG recordings and behavioural ratings from 51 participants who read and evaluated 210 short English-language texts — 70 Haiku (nature-themed), 70 Senryu (emotion-themed), and 70 non-poetic Control texts. Each poem/text was rated on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativ

Participants

4

Size

4.29 GB

Updated

Jun 6, 2026

# Multimodal Sensor Fusion for EEG-Based BCI Typing Systems ## Dataset Overview This dataset contains recordings of EEG and EyeTracking for a BCI spelling task. The data were collected in 2023 at Northeastern University. - N=21 - Calibration task were proctored using BciPy [1] - The dataset is organized in accordance with the Brain Imaging Data Structure (BIDS) specification (version 1.7.0). ## Methodology Calibration data were collected from control participants (n=21, mean age 23.6 ± 3.1

Participants

21

Size

442 MB

Updated

Jun 6, 2026

NOTE: This version contains datasets for 49 of the 69 participants. New versions will be created as more data are uploaded. This dataset contains the fMRI and EEG data for E.A. McDevitt, G. Kim, N.B. Turk-Browne, K.A. Norman (2026). The role of rapid eye movement sleep in neural differentiation of memories in the hippocampus. Journal of Cognitive Neuroscience, 10.1162/jocn.a.82 Please refer to the paper for detailed methods. The dataset includes 69 participants with three fMRI scans and one

Participants

49

Size

464 GB

Updated

Jun 6, 2026