Bimodal dataset on Inner speech
…Multimodal datasets of brain data enable the fusion of neuroimaging modalities with…
- Participants
- 4
- Channels
- 64 (10-10)
- Citations
- 27
- Size
- 9.33 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
100 results for "hybrid neuroimaging" · page 7 of 10 · ranked by relevance
…Multimodal datasets of brain data enable the fusion of neuroimaging modalities with…
This dataset comprises EEG recordings from 10 patients with spinal cord injury performing five distinct hand and forearm motor imagery tasks: hand open, palmar grasp, lateral grasp, pronation, and supination. The study investigates the decoding of attempted movements from low-frequency EEG signals (movement-related cortical potentials) to support neuroprosthetic applications for upper limb control. Data include 360 trials (72 per class) acquired at 256 Hz with 61 EEG channels and 3 EOG channels, preprocessed with ICA-based artifact rejection and bandpass filtering.
[ paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.
[ recordings from 28 healthy adults performing a sustained attention gradual-onset continuous performance task (gradCPT), along with resting-state, checkerboard, and imagery task data. The dataset supports multimodal investigation of sustained attention and its neural correlates using combined electrophysiological, hemodynamic, and structural connectivity measures.
[ recordings from patients performing a forced two-choice response task, collected in the epilepsy monitoring unit at Oregon Health & Science University. The data extends characterization of movement-related neural oscillations using intracranial electrode recordings, providing insights into canonical motor-related activity patterns across distributed brain regions.