Discover datasets

This dataset comprises simultaneous EEG and motion capture recordings from 20 healthy adults performing a spatial orientation task involving full-body heading changes. Participants completed a spot rotation task under two conditions: joystick-controlled visual rotation on a 2D display and physical full-body rotation in a virtual reality environment. The dataset includes 157-channel EEG data (129 scalp + 28 neck electrodes) sampled at 1000 Hz and multi-camera motion capture data at 90 Hz, enabling investigation of cortical dynamics and sensorimotor integration during naturalistic spatial navigation.

Participants

20

Channels

160 (other)

Citations

39

Size

61.4 GB

Version

v1.0.0

Updated

Jul 10, 2026

…Bayesian optimization, individual experimental parameters, brain-computer interfaces, learning from small data…

Participants

13

Citations

2

HED

v8.4.0

Size

2.82 GB

Version

v1.0.0

Updated

Jul 10, 2026

A comprehensive EEG database containing electroencephalographic signals from 87 healthy participants performing motor imagery brain-computer interface tasks. The dataset comprises over 20,800 trials (~70 hours of recording) organized into three datasets (A, B, C) using a standardized Graz protocol for right and left hand motor imagery. In addition to raw EEG signals, the database includes detailed participant demographics, personality profiles, cognitive traits, and BCI performance metrics, enabling research on user-performance relationships, cross-user machine learning algorithms, and profile-informed signal classification.

Participants

87

Channels

27 (10-10)

Citations

38

HED

v8.4.0

Size

15.8 GB

Version

v1.0.4

Updated

Aug 18, 2026

…signal processing ## Abstract Brain-computer interfaces (BCIs) have been gaining momentum in…

Participants

11

Channels

14 (10-10)

Citations

39

HED

v8.4.0

Size

277 MB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset contains electroencephalogram (EEG) recordings from 19 healthy participants using a brain-computer music interface designed to enable real-time control of musical tempo through motor imagery. Participants performed kinesthetic motor imagery tasks—imagining squeezing a ball to increase tempo or relaxing to decrease tempo—across nine experimental runs including a calibration phase. The data were collected at 1 kHz sampling rate with 20-second epochs synchronized to music clips, providing a resource for investigating the neural correlates of intentional tempo modulation and music-based brain-computer interface design. This dataset accompanies the publication by Daly et al. (2018). Full methodological details are available in Daly et al. (2014a, 2014b).

Participants

18

Channels

19 (10-20)

Size

2.39 GB

Version

v1.0.0

Updated

Jul 10, 2026

…Open access dataset for hybrid brain-computer interfaces using EEG and NIRS…

Participants

29

Channels

30 (10-05)

Citations

94

HED

v8.4.0

Size

6.62 GB

Version

v1.0.3

Updated

Aug 18, 2026

This open-access EEG dataset comprises multi-session, multi-task recordings from 15 healthy participants performing resting state and graded difficulty levels of the MATB-II task. Acquired at 500 Hz using 62 active electrodes, the dataset includes 90 trials per participant across two sessions and is designed to support passive brain-computer interface research and mental workload estimation in neuroergonomic applications.

Participants

15

Channels

61 (10-10)

Citations

64

HED

v8.4.0

Size

4.04 GB

Version

v1.0.3

Updated

Aug 18, 2026

A motor imagery brain-computer interface dataset comprising EEG recordings from 30 healthy participants performing left-hand grasping imagery and rest tasks, augmented with pupillometry measurements. The dataset includes 32-channel EEG data sampled at 512 Hz acquired using BioSemi ActiveTwo hardware, with two experimental runs per subject containing 50 trials total. This derivative dataset was processed using the Mother of All BCI Benchmarks (MOABB) framework and is designed to support BCI research and algorithm development.

Participants

30

Channels

32 (10-10)

Citations

37

HED

v8.4.0

Size

3.22 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises EEG recordings from 15 healthy subjects performing six different upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) and rest conditions in both movement execution and motor imagery modalities. The study investigates neural encoding of individual upper limb movements using low-frequency EEG signals (0.3-3 Hz) and achieves classification accuracies of 55-87% for executed movements and 27-73% for imagined movements. Source localization analysis identifies discriminative movement information in premotor areas, primary motor cortex, somatosensory cortex, and posterior parietal cortex, with applications toward non-invasive control of motor neuroprostheses and robotic arms.

Participants

15

Channels

61 (10-05)

Citations

94

HED

v8.4.0

Size

34.0 GB

Version

v1.0.3

Updated

Aug 18, 2026

…Riemannian geometry, Online, Asynchronous, Brain-Computer Interfaces, Steady State Visually Evoked Potentials…

Participants

12

Channels

8 (10-10)

Citations

74

HED

v8.4.0

Size

160 MB

Version

v1.0.1

Updated

Aug 18, 2026