Discover datasets

This dataset contains EEG recordings from a picture-word interference (PWI) naming study conducted at San Diego State University's NeuroCognition Laboratory. Participants named line drawings of objects preceded by semantically related, identity-related, or unrelated English distractor words, while 32-channel EEG and vocal responses were recorded. The dataset supports investigation of lexical-semantic access and word retrieval processes during picture naming.

Participants

29

Channels

32 (10-10)

Size

2.28 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises multimodal neuroimaging data from a bifocal transcranial magnetic stimulation (TMS) study investigating cortico-cortical paired associative stimulation (ccPAS) and its effects on interhemispheric motor cortex connectivity. Participants underwent TMS-EEG recordings targeting bilateral primary motor cortex with varying interstimulus intervals (4, 9, and 14 ms), combined with structural and functional MRI acquisitions. The dataset includes 64-channel EEG recordings, resting-state fMRI, high-angular-resolution diffusion imaging, and anatomical MRI to characterize spike-timing-dependent plasticity mechanisms in human motor networks.

Participants

13

Channels

64 (10-10)

Citations

7

Size

1021 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises simultaneous EEG-fMRI recordings from 22 participants (ages 23-51) during naturalistic viewing, collected at the Nathan Kline Institute. The 64-channel EEG data (61 cortical channels, 2 EOG, 1 ECG) was acquired concurrently with fMRI, supplemented by eye-tracking and respiratory recordings. The dataset includes demographic information and behavioral assessments (sleep quality, caffeine intake) to investigate correlations between electrical brain activity and hemodynamic fluctuations during naturalistic stimulation.

Participants

22

Channels

61 (10-10)

Citations

41

Size

256 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises longitudinal motor imagery EEG recordings from 18 BCI-naive subjects across six sessions (one offline, five online), designed to study transfer learning and skill acquisition in brain-computer interfaces (BCIs). It compares two domain adaptation frameworks—Generic Recentering and Personally Assisted Recentering—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were collected at 512 Hz with 22 EEG channels and analyzed using Riemannian geometry-based classifiers.

Participants

18

Channels

22 (10-10)

Citations

49

HED

v8.4.0

Size

14.4 GB

Version

v1.0.4

Updated

Aug 20, 2026

This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.

Participants

12

Channels

32 (egi-geodesic)

Citations

44

HED

v8.4.0

Size

52.0 GB

Version

v1.0.3

Updated

Aug 20, 2026