Discover datasets

This dataset contains multimodal brain–machine interface (BMI) recordings from seven healthy adults who trained over nine longitudinal sessions to control a lower-limb exoskeleton via motor imagery. It includes 60-channel EEG, 4-channel EOG, dual IMU motion data, and exoskeleton control/feedback signals collected during open-loop calibration and closed-loop walk/stop trials. The dataset supports research on EEG-based decoding of motor imagery for neurorehabilitation and human-robot interaction applications.

Participants

7

Channels

60

Citations

2

Size

3.62 GB

Version

v1.0.0

Updated

Aug 18, 2026

This dataset comprises electroencephalographic recordings from 32 participants performing motor imagery tasks during sit-to-stand and stand-to-sit transitions in both offline and online brain-computer interface (BCI) paradigms. Participants completed guided motor imagery trials while seated or standing, with EEG signals recorded from 17 channels at 250 Hz. The dataset includes offline calibration phases used to train machine learning classifiers and corresponding online validation phases where real-time BCI decoding was performed, providing a comprehensive resource for investigating neural correlates of postural transitions and BCI performance.

Participants

32

Channels

17 (10-10)

Size

2.03 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises raw 64-channel EEG, ECG, PPG, and behavioral data collected from 30 healthy young adults during resting-state and verbal working memory tasks. Participants completed a digit-span serial recall task under four stimulus presentation modes (simultaneous, fast, fast+delay, and slow sequential), enabling investigation of neural and physiological correlates of encoding, maintenance, and retrieval. The dataset supports research on working memory load classification and the relationship between oscillatory brain activity, autonomic signals, and behavioral performance.

Participants

30

Channels

63 (10-10)

Citations

5

Size

41.4 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises two reversal learning tasks designed to investigate differential neural processing of reward gain and punishment avoidance. Participants performed parallel tasks using distinct reinforcers: monetary reward (gain/non-gain outcomes) and primary threat avoidance (noise burst avoidance/application), with noise intensity individually titrated to match subjective value of monetary rewards. The dataset enables investigation of how the brain differentially encodes appetitive versus aversive learning contingencies.

Participants

26

Channels

66 (10-10)

Citations

20

Size

31.5 GB

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 comprises MEG, MRI, EMG, and eye-tracking data from 31 healthy right-handed adults performing a sequential finger tapping task (SFTT) to study early motor skill learning, overnight consolidation, and longer-term retention. Participants completed an initial MEG training session followed by up to two retest sessions approximately 24 hours and 30 days later, with resting-state MEG collected before and after each practice block. The dataset supports investigation of neural correlates of motor skill acquisition and memory consolidation.

Participants

31

Citations

3

Size

95.8 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises multimodal physiological recordings from 86 participants during resting state and a digit span working memory task. It includes 64-channel EEG, electrocardiography, photoplethysmography, pupillometry, and behavioral performance data. The dataset enables investigation of neural and peripheral physiological correlates of cognitive load, working memory capacity, and cognitive overload detection across fine temporal scales.

Participants

86

Channels

63 (10-10)

Citations

61

Size

253 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises raw multimodal recordings from 30 healthy right-handed adults performing a hierarchy of cognitive, motor, and combined cognitive-motor tasks across three sessions. Data include EEG, fNIRS, ECG, EMG, torque/kinematic, behavioral (push-button), and subjective (sleepiness, cognitive load) measures, organized according to the BIDS standard. The dataset supports research into neurophysiological correlates of cognitive-motor interactions and dual-task performance.

Participants

30

Channels

32 (10-10)

Citations

1

Size

4.21 GB

Version

v1.0.0

Updated

Aug 18, 2026

Participants

24

Channels

64

HED

v8.4.0

Size

5.69 GB

Version

v1.0.0

Updated

Jul 10, 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