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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

We presented twenty adult participants with harmonic complex sound (HCS) stimuli that varied in frequency in an auditory oddball protocol during simultaneous EEG and MEG recording (for details, see Inbar et al, in press) Note: Participant identifiers differ... References ---------- Inbar, T.C., Badier, J.M., Bénar, C. G., Kanzari, K., Besson, M. & Chanoine, V. (in press). Pre-attentive pitch processing of harmonic complex sounds at sensor and source levels: comparing simultaneously recor

Participants

19

Size

11.2 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

## Overview This dataset was originally published in Gramann, K., Töllner, T. and Müller, H.J. (2010), Dimension-based attention modulates early visual processing. Psychophysiology, 47: 968-978. https://doi.org/10.1111/j.1469-8986.2010.00998.x It was subsequently used to investigate automatic labeling of independent components in ICA and is referred to as the "Cue" dataset: Frølich, L., Andersen, T.S. and Mørup, M. (2015), Multi-class classification of ICS of EEG. Psychophysiol, 52: 32-45. https

Participants

12

Size

5.63 GB

Updated

Jun 6, 2026

# README # WARNING Below is a template to write a README file for this BIDS dataset. If this message is still present, it means that the person exporting the file has decided not to update the template.If you are the researcher editing this README file, please remove this warning section. The README is usually the starting point for researchers using your dataand serves as a guidepost for users of your data. A clear and informativeREADME makes your data much more usable. In general you can inclu

Participants

47

Size

12.1 GB

Updated

Jun 6, 2026

This dataset contains high-density EEG recordings collected during a visual stimulation task. Files are organized according to the EEG-BIDS specification. Raw data are BrainVision (.vhdr/.vmrk/.eeg). Task: visual Session: ses-01 Provenance: Converted from c:/shared/raw_eeg with this helper script. No acquisition-time filters applied (offline preprocessing not included here).

Participants

31

Size

17.6 GB

Updated

Jun 6, 2026

Introduction: The EEG data was recorded using the 128-channel Amps 300 amplifier (Electrical Geodesics Inc., OR, USA) at a sampling frequency of 1000 Hz. The EEG data acquisition was conducted during the resting. Structural MRI data for the same participants were acquired at the University of Oklahoma Health Science Center (OUHSC) MRI facility using a GE MR750 scanner. The scans were obtained with GE’s BRAVO sequence, with a field of view (FOV) of 240 mm and 180 axial slices per slab Preproce

Participants

34

Size

3.00 GB

Updated

Jun 6, 2026

In this dataset we included iEEG recordings of responses to 41 intracranial high frequency stimulations evoking negative motor responses, in 23 patients undergoing stereo-EEG presurgical evaluation for drug-resistant epilepsy. The dataset contains 24 seconds of iEEG data around each stimulation, 9-10 seconds before the start of the stimulation, up to 5 seconds of intracranial stimulation and 9-10 seconds after the end of the stimulation. Each recording contains two 5 second epochs, pre-s

Participants

21

Size

1.04 GB

Updated

Jun 6, 2026