65 By ANT
…Participants underwent laser stimulation and subsequently verbally rated the intensity of pain…
- Participants
- 65
- Channels
- 32 (10-10)
- Size
- 42.9 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
100 results for "deep brain stimulation" · page 8 of 10 · ranked by relevance
…Participants underwent laser stimulation and subsequently verbally rated the intensity of pain…
…oscillations using deep learning: a reverse engineering approach. Brain Commun. 2021 Nov…
Imported from OpenNeuro ds003801
…Participants underwent laser stimulation and subsequently verbally rated the intensity of pain…
…or randomized flicker as sham stimulation, while subjects performed a psychomotor vigilance…
An EEG dataset of imagined speech from 15 healthy participants comparing traditional cue-based and gamified paradigm designs for brain-computer interface applications. The dataset comprises 1,800 trials of four Spanish directional commands recorded at 500 Hz from 24 electrodes using the mBrainTrain Smarting system. This derivative dataset enables systematic evaluation of paradigm design effects on imagined speech decoding performance.
…or randomized flicker as sham stimulation, while subjects performed a psychomotor vigilance…
This dataset comprises EEG recordings from 15 healthy participants performing reach-and-grasp motor imagery tasks using three different electrode systems: gel-based laboratory equipment, water-based mobile EEG, and dry-electrode mobile EEG. Data were acquired at 256 Hz from 58 EEG channels plus 6 EOG channels across three sessions with 7200 total trials. Participants executed palmar grasp (toward glass) and lateral grasp (toward spoon) actions. The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.
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.
…temporal_features, deep_learning - **Frequency bands**: bandpass=[2.0, 30.0] Hz…