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This dataset contains cue-locked EEG recordings and behavioral event annotations from 43 healthy adults performing a random-dot kinematogram (RDK) task designed to dissociate low-level and high-level hierarchical priors during perceptual decision-making. The study examines how such priors bias behavior via signal detection theory and generalized drift-diffusion modeling, and how they modulate occipital oscillatory and aperiodic EEG activity. Participants also completed questionnaires assessing psychosis proneness and psychological flexibility traits.

Participants

43

Channels

31 (10-10)

Size

8.76 GB

Version

v1.0.0

Updated

Aug 18, 2026

This dataset comprises EEG recordings from participants performing go-nogo categorization and detection tasks across two recording sessions. Participants viewed briefly presented images (20 ms) and responded according to task-specific rules: lifting a finger for target stimuli (go response) or maintaining button press for non-targets (nogo response). The dataset includes two complementary tasks—animal categorization and image recognition—with systematic manipulation of stimulus presentation and response requirements, providing a rich resource for investigating event-related potentials and decision-making processes.

Participants

14

Channels

31 (10-20)

Size

9.22 GB

Version

v1.0.0

Updated

Jul 10, 2026

…The detailed description of the dataset is currently under working by Hong…

Participants

23

Channels

66 (10-10)

Size

276 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises EEG recordings from 54 healthy participants performing a P300-based brain-computer interface speller task, designed to investigate BCI illiteracy. The study includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with participants completing offline training and online test phases using a 36-symbol row-column speller paradigm. The dataset achieved 96.7% accuracy with an 11.1% BCI illiteracy rate, providing a comprehensive resource for evaluating P300-based BCI performance and individual differences in BCI competence.

Participants

54

Channels

62 (10-05)

Citations

100

HED

v8.4.0

Size

132 GB

Version

v1.0.4

Updated

Aug 18, 2026

This dataset investigates the neural representation of consciously seen and unseen information, likely using EEG recordings to examine visual consciousness and perception. It is designed to support research into the neural correlates of conscious versus unconscious processing of visual stimuli.

Participants

33

Channels

63 (10-10)

Citations

1

Size

44.4 GB

Version

v1.0.0

Updated

Aug 19, 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

This dataset comprises electroencephalography (EEG) recordings from 16 participants viewing object images presented at eight different 2-D rotations in rapid visual streams at two presentation rates (5 Hz and 20 Hz). The study investigates rotation-tolerant neural representations and their temporal dynamics during high-level object processing, providing insights into how the visual system encodes object identity invariant to viewpoint changes.

Participants

16

Size

18.3 GB

Version

v1.0.0

Updated

Jul 10, 2026

…N2pc, ERP, Canonical difference waves - **Spatial filters**: CCA spatial filters ## Cross-Validation…

Participants

18

Channels

30 (10-10)

Citations

26

HED

v8.4.0

Size

1.82 GB

Version

v1.0.2

Updated

Aug 18, 2026

…To map the brain’s spatial organization supporting this process, we previously…

Participants

32

Channels

62 (10-10)

Citations

5

Size

221 GB

Version

v1.0.0

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