Discover datasets

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 dataset contains EEG/ERP data from an experiment examining sensitivity to semantic and syntactic violations in deaf readers. Participants read sentences presented word-by-word (RSVP) that contained subject-verb agreement violations, semantic violations, combined violations, word-order violations, or correct control sentences, while EEG was recorded. The study aims to characterize how deaf readers process linguistic anomalies at the neural level.

Participants

62

Channels

32 (10-10)

Citations

2

Size

8.53 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains electroencephalographic (EEG) recordings from 24 participants performing object classification and passive viewing tasks while viewing static images of objects with varying capacities for movement and aliveness. The study investigates how the human brain represents movement as a fundamental organizational principle in visual object processing, independent of animacy. This is Experiment 3 (EEG: movement) from a multi-experiment study. Raw EEG data in BrainVision format, preprocessed derivatives in EEGlab and cosmoMVPA formats, analysis scripts, stimuli, and decoding results are provided. Note: Analysis code requires both Experiment 2 and Experiment 3 datasets to be downloaded for full replication of analyses.

Participants

24

Citations

2

Size

80.7 GB

Version

v1.0.0

Updated

Jul 10, 2026

The BCIT Auditory Cueing dataset comprises EEG, eye-tracking, and vehicle performance data collected during a prolonged simulated driving task designed to assess driver fatigue and cognitive state. Subjects performed two 45-minute driving conditions with either random or predictive auditory cues preceding lateral vehicle perturbations, while continuous measurements of steering behavior, reaction times, and subjective fatigue scales were recorded. This dataset extends prior driving research by examining the relationship between neural biomarkers and fatigue-related performance decrements in a controlled, high-frequency perturbation environment.

Participants

17

Channels

64 (10-10)

HED

v8.0.0

Size

20.4 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains electroencephalographic (EEG) recordings from 24 participants performing object classification and passive viewing tasks in Experiment 1 of a study investigating how the brain represents object movement and aliveness. Participants classified static images of objects with varying capacities for movement (moving/non-moving) and animacy (alive/not alive) as either alive or capable of movement. The study demonstrates that movement capacity is a significant organizational principle in visual object representation, independent of animacy.

Participants

24

Citations

15

Size

82.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises scalp EEG recordings from 22 participants who used BrainForm, a gamified brain-computer interface (BCI) designed for P300 event-related potential (ERP) training and scalable data collection with consumer hardware. Participants performed repeated runs of a P300 spelling/selection task, allowing analysis of BCI skill acquisition across sessions and the effects of different visual stimulation textures on performance. The dataset supports research on BCI training, human factors, and machine learning applications on ERP data.

Participants

22

Channels

8 (10-10)

Citations

1

HED

v8.4.0

Size

237 MB

Version

v1.0.6

Updated

Aug 20, 2026

The Brain, Body, and Behaviour Dataset - Experiment 3 is a multimodal neurophysiological dataset comprising 29 subjects across 2 sessions designed to investigate the effects of attentional state on learning from educational videos. Participants watched six educational videos under two conditions: attentive (with post-video testing) and distracted (with concurrent cognitive load), while simultaneous recordings of brain activity, cardiovascular function, eye movements, and head motion were collected. The dataset includes concurrent EEG, ECG, EOG, gaze tracking, pupil size, and head position data, along with behavioral measures including memory questionnaires and ADHD symptom assessments.

Participants

29

Channels

64 (10-10)

Size

7.44 GB

Version

v1.0.0

Updated

Jul 10, 2026

A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.

Participants

54

Channels

62 (10-05)

Citations

99

HED

v8.4.0

Size

121 GB

Version

v1.0.5

Updated

Aug 18, 2026

This dataset comprises simultaneous EEG and fNIRS recordings from 12 participants performing semantic imagery tasks involving silent naming and sensory-based imagination of animals and tools. Participants engaged in visual, auditory, and tactile perception tasks while neural activity was captured using a 64-channel BioSemi EEG system and a NIRx fNIRS imaging system with integrated optodes. The multimodal neuroimaging data supports research in semantic decoding and brain-computer interface applications.

Participants

12

Channels

64 (biosemi)

Size

24.2 GB

Version

v1.0.0

Updated

Jul 10, 2026