EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz
Imported from OpenNeuro ds004018
- Participants
- 16
- Citations
- 98
- Size
- 10.5 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
100 results for "Sternberg task" · page 9 of 10 · ranked by relevance
Imported from OpenNeuro ds004018
…Detailed task description and subject instructions can be found in a seperate…
This dataset is a NEMAR re-host and BIDS conversion of the emg2pose benchmark, comprising surface electromyography (EMG) recordings paired with hand pose labels for wrist-worn sEMG-based hand pose estimation. It includes 16-channel, 2 kHz sEMG recordings synchronized with joint-angle labels derived from a 26-camera motion-capture rig, spanning 193 users, 370 hours, and approximately 80 million labeled frames across 29 behavioral stages.
…The driving practice task lasted 10-15 min, until asymptotic performance in…
…This is not that task. This is a Probabilistic Selection Task. These…
This dataset contains neuroimaging data organized in BIDS format, comparing pre- and post-intervention groups. It is intended to support analyses of changes associated with an experimental intervention, providing raw data for reuse by the research community.
…The task was performed using identical systems at three different sites: - Army…
This dataset contains EEG recordings from a flanker word/pseudoword lexical decision task collected at the NeuroCognition Laboratory in San Diego. Participants viewed four-letter real words and pseudowords flanked by identical, different, or no flanking words, while EEG was recorded to examine neural responses associated with lexical processing under flanker conditions. The dataset was collected under San Diego State University's IRB oversight.
…Also behavioral files from the task, which contain more trial-specific information…
This dataset contains SSVEP (Steady-State Visually Evoked Potential) EEG recordings from 12 healthy subjects seated in an exoskeleton-equipped wheelchair, collected as part of E. Kalunga's PhD research at the University of Versailles. Subjects focused on LED panels flickering at 13, 17, and 21 Hz, or on a fixation point (reject/rest class), across 1–5 sessions per subject. The dataset was designed for developing online SSVEP-based brain-computer interface (BCI) systems using Riemannian geometry, with applications toward assistive robotics.