Discover datasets

MEG-SCANS is a magnetoencephalography (MEG) dataset comprising raw and MaxFiltered recordings from 24 German-speaking participants performing an auditory speech experiment. Stimuli included audiobooks, Oldenburger Matrix Sentence Test sentences at varying intelligibility levels for speech reception threshold assessment, and chirp stimuli for signal quality checks. The dataset also includes structural MRI scans, hearing screening results, empty-room recordings, and audio stimulus materials, organized in BIDS format to support encoding/decoding analyses of speech-evoked brain responses.

Participants

24

Citations

1

Size

204 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises simultaneous scalp EEG and intracranial EEG (iEEG) recordings from fifteen epilepsy patients undergoing intracranial monitoring while performing a modified Sternberg verbal working memory task. Recordings include depth electrode iEEG, 10-20 scalp EEG, electrode localization (MNI coordinates and anatomical labels), and derived LCMV beamforming virtual sensor data from multiple brain regions (temporal superior lobe, lateral prefrontal cortex, occipital cortex, posterior parietal cortex, and Broca's area). Behavioral data on trial set size, match/mismatch condition, accuracy, and response time are also provided, enabling analyses of memory encoding, maintenance, and recall processes, connectivity, and neural replay.

Participants

15

Channels

19 (10-20)

Citations

47

Size

11.9 GB

Version

v1.0.0

Updated

Aug 19, 2026

This magnetoencephalography (MEG) dataset comprises neuroimaging data collected to establish standardized protocols for cross-site pooling of MEG data. The dataset demonstrates the application of BIDS formatting to MEG neuroimaging, facilitating data harmonization and reproducible analysis across multiple research institutions.

Participants

9

Citations

17

Size

77.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises intracranial electrocorticography (ECoG) recordings from 9 epilepsy patients implanted with grid, depth, and strip electrodes (1,330 electrodes total), collected while participants listened to a 30-minute naturalistic story containing over 5,000 words. It provides raw and minimally preprocessed (high-gamma band) neural data along with aligned auditory stimuli, word-level transcripts, and linguistic features spanning low-level acoustics to large language model embeddings. The dataset is intended to support research on natural language comprehension using high-fidelity invasive recordings and includes tutorials replicating prior findings.

Participants

9

Citations

4

Size

14.4 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains long-term intracranial EEG (iEEG) recordings from 13 patients with focal epilepsy, part of the larger RESPect registry collected at the University Medical Center Utrecht. Recordings include electrocorticography (5 patients) and stereo-electroencephalography (8 patients), organized according to the BIDS specification with detailed session and run metadata reflecting monitoring periods and clinical protocols such as SPESclin. The dataset supports investigations into structural and effective brain connectivity in focal epilepsy.

Participants

13

Size

44.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

The MNE-Sample-Data dataset comprises simultaneous MEG and EEG recordings acquired from a single subject using a Neuromag Vectorview system at the Martinos Center for Biomedical Imaging. The experiment involved visual stimulation (checkerboard patterns presented to left and right visual fields) and auditory stimulation (tones to left and right ears), with occasional face stimuli requiring motor responses. Structural MRI data from a 1.5 T Siemens scanner and Freesurfer-derived anatomical derivatives are included, providing a comprehensive reference dataset for MEG/EEG analysis and method development.

Participants

2

Size

1.93 GB

Version

v1.0.0

Updated

Jul 10, 2026

A high-density 124-channel EEG dataset comprising event-related potentials (ERPs) from 10 healthy participants during a visual object recognition task. Participants viewed 5,184 photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects), with 72 photographs per category, presented for 500 ms each. The dataset is suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.

Participants

10

Channels

124 (egi-geodesic)

Citations

93

HED

v8.4.0

Size

3.40 GB

Version

v1.0.3

Updated

Aug 18, 2026

FOODEEG is an open dataset of electroencephalographic (EEG) and behavioural responses to food images from 117 participants across two testing sessions. Session 1 involved a food categorisation task with continuous EEG recording, while Session 2 comprised a food go/no-go task and a food paired choice task, along with questionnaires on dietary style and eating motivations. The dataset also includes normative ratings on 22 food attributes collected from an independent online sample (N = 624), providing a rich resource for studying neural and behavioural correlates of food-related decision-making.

Participants

117

Size

91.1 GB

Version

v1.0.0

Updated

Aug 19, 2026

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 64-channel EEG recordings from 11 healthy participants performing an auditory P300-based brain-computer interface task using selective attention to multiple tone streams. Participants attended to one of three auditory streams of musical tones and counted target stimuli while P300 event-related potentials were recorded. Classification using Riemannian geometry achieved >80% accuracy for 5 subjects and >75% for 9 subjects, demonstrating the feasibility of multi-class auditory BCIs based on auditory stream segregation.

Participants

11

Channels

64 (10-10)

Citations

11

HED

v8.4.0

Size

6.31 GB

Version

v1.0.4

Updated

Aug 18, 2026