Discover datasets

This dataset contains continuous EEG, ECG, and EDA recordings from 63 participants who viewed and rated Streetview images of urban environments across nine subjective scales (e.g., valence, safety, beauty). The study aims to investigate how urban environmental features influence subjective appraisal and neural responses such as stimulus-locked ERPs. Data were collected in 2024 at the BeMoBIL lab, TU Berlin.

Participants

63

Channels

66 (other)

Citations

3

Size

34.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

Imported from OpenNeuro ds005628

Participants

102

Channels

8 (10-10)

Citations

1

Size

633 MB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains EEG recordings from 34 participants collected to investigate the adaptive recruitment of cortex-wide recurrent processing during visual object recognition. Participants viewed a stimulus set of 242 images, comprising 'challenge' and 'control' images selected based on discrepancies between human behavioral performance and AlexNet classification, while performing a rapid serial visual presentation task with a paper-clip detection component. The dataset includes derivatives with time-resolved decoding accuracy matrices for object identity, supporting analyses of recurrent cortical dynamics in visual processing.

Participants

34

Channels

63 (10-10)

Citations

1

Size

61.2 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains EEG recordings from elderly patients with dementia undergoing 40Hz auditory gamma-band entrainment sessions. The study investigates whether auditory stimulation modulated at 40Hz can entrain brain oscillations and enhance functional connectivity, particularly within the default mode network, as a potential intervention for Alzheimer's disease symptoms. Both short and long entrainment sessions were conducted to accommodate participant tolerance, with EEG data collected using a 19-channel 10/20 system setup.

Participants

35

Channels

19 (10-20)

Citations

32

Size

356 MB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises electroencephalography (EEG) recordings collected to investigate the relationship between auditory streaming—the perceptual organization of sound sequences—and interoceptive awareness. The study examines how the brain processes complex auditory stimuli and integrates this information with internal bodily signals, contributing to our understanding of sensory integration and conscious perception.

Participants

22

Channels

1 (other)

Size

7.25 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises auditory neurophysiological recordings investigating the relationship between auditory streaming perception and interoceptive awareness. Participants engaged in auditory tasks while electrophysiological signals were recorded, enabling examination of how the brain processes complex auditory scenes and integrates bodily state information during perceptual organization.

Participants

22

Channels

1 (other)

Size

7.25 GB

Version

v1.0.0

Updated

Jul 22, 2026

This MEG dataset comprises auditory cortex mapping data from a single subject acquired using a CTF 275 system at 120 kHz sampling rate. The experiment involved passive listening to 1,795 iso-intensity pure tones ranging from 100 Hz to 21.5 kHz presented binaurally over a 12-minute acquisition run. The dataset includes anatomical MRI data processed with FreeSurfer and serves as a reference example for MEG-based auditory cortex mapping techniques.

Participants

2

Citations

2

Size

15.5 GB

Version

v1.0.0

Updated

Jul 10, 2026

NEMAR Dataset nm000104: emg2qwerty - EMG-based typing detection

Participants

108

Citations

1

Size

223 GB

Version

v2.0.0

Updated

Jul 10, 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 comprises EEG recordings from a P300 visual matrix speller study comparing three unsupervised learning methods (Expectation-Maximization, Learning from Label Proportions, and their combination MIX) for brain-computer interface decoding. Twelve healthy participants performed a copy-spelling task using a modified 6×6 character grid extended with 10 # symbols as visual blanks (46 total symbols), recorded at 1000 Hz from 31 EEG channels. The study demonstrates that unsupervised learning methods can achieve performance comparable to supervised approaches without requiring calibration data.

Participants

12

Channels

31 (10-10)

Citations

26

HED

v8.4.0

Size

4.81 GB

Version

v1.0.2

Updated

Aug 18, 2026