Discover datasets

The PEARL-Neuro Database contains EEG, fMRI, genetic, health, and lifestyle data from 192 healthy middle-aged (50-63) Polish individuals at risk of Alzheimer's disease, based on APOE and PICALM genotypes. A subgroup of 79 participants also underwent neuroimaging (EEG and fMRI) during resting-state and cognitive task protocols (multi-source interference task and Sternberg's memory task), alongside blood test data. The dataset aims to support research on genetic, lifestyle, and neuroimaging correlates of dementia risk in middle-aged adults.

Participants

79

Citations

36

Size

240 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains intracranial EEG recordings and behavioral event data from patients performing a delayed free recall task, in which participants studied word lists, engaged in an arithmetic distractor task, and freely recalled words. Data were collected across multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania, providing electrode localization and referencing information suitable for memory and epilepsy research.

Participants

280

Citations

2

Size

576 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains electroencephalography (EEG) recordings collected during reactive balance perturbations to investigate cortical contributions to feedback-driven sensorimotor control. The data support analyses of how specific cortical regions contribute to reactive postural responses following balance disturbances. It is intended to inform understanding of the neural mechanisms underlying balance control and their potential relevance to rehabilitation research.

Participants

17

Channels

32 (10-10)

Citations

1

Size

351 MB

Version

v1.0.0

Updated

Aug 19, 2026

This phantom EEG dataset contains recordings from an electrically conductive head model under six controlled conditions: brain-only baseline, and brain signals combined with eye, jaw muscle, neck muscle, motion, or simultaneous artifacts. The dataset includes ground truth brain signals and accompanying EEGLAB scripts for artifact detection validation and parameter optimization, supporting development and benchmarking of EEG artifact removal methods.

Participants

1

Channels

128 (other)

Citations

27

Size

10.8 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises intracranial EEG recordings collected to support the development and validation of automatic evoked response detection (ER-Detect) methods. Data were gathered from patients undergoing electrical stimulation mapping at Mayo Clinic and University Medical Center Utrecht, aiming to characterize cortico-cortical evoked potentials for clinical and research applications in epilepsy and brain connectivity studies.

Participants

14

Citations

1

Size

30.2 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 dataset contains MEG phantom recordings designed to characterize deep brain stimulation (DBS) device-specific artefacts in magnetoencephalography. Using a Neuromag phantom setup, movement onset was captured with an accelerometer to assess artefact behavior associated with DBS hardware. The data support a study investigating how DBS devices affect MEG signal quality and interpretation.

Participants

4

Size

6.13 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset presents a multimodal collection of naturalistic fMRI, structural MRI, and EEG data from 52 healthy older Cantonese-speaking adults (over 65 years) listening to excerpts from 'The Little Prince' in Cantonese. It aims to address the underrepresentation of non-Indo-European languages and older populations in language neurobiology research, providing insights into brain-behavior relationships and healthy aging. The dataset includes rich behavioral, linguistic, and cognitive measures alongside detailed audio and text annotations.

Participants

52

Channels

64

Citations

3

Size

109 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains behavioral events and electrophysiological recordings from a hybrid spatial-navigation and free recall experiment conducted at the University of Pennsylvania (2021-2022). Participants performed a virtual courier task delivering items across a town, followed by recall testing. The experiment comprised two phases: read-only sessions for generating classifier training data, and closed-loop sessions where stimulus presentation timing was optimized based on real-time neural predictions of memory encoding. The dataset supports investigation of spatial memory dynamics and the efficacy of classifier-based closed-loop stimulation for memory enhancement.

Participants

34

Channels

128 (biosemi)

Citations

5

Size

1.30 TB

Version

v1.0.0

Updated

Jul 10, 2026

This stereoelectroencephalography (sEEG) dataset comprises intracranial recordings from neurosurgical patients passively listening to natural conversational speech. Participants heard 30-45 second passages of speech and answered comprehension questions, with data collected across 6 blocks of 7 passages each. The dataset includes depth electrodes and, for two patients, grid and strip electrodes, providing high-resolution neural activity during speech perception. USAGE RESTRICTIONS: These data may not be used for commercial purposes, including but not limited to use in any kind of training set for commercial machine learning applications. These data may not be used in any way that either in part or in whole disambiguates participant identity, including but not limited to attempts at 3D facial reconstruction.

Participants

10

Size

13.2 GB

Version

v1.0.0

Updated

Jul 10, 2026