100 results for "neural probe validation" · page 9 of 10 · ranked by relevance
…bidsignore (files to ignore in validation) │ ├── code/ (analysis and preprocessing code) │ ├── preprocessing…
…compliant datasets to ensure technical validation and facilitate upload to OpenNeuro. Poetry…
…The data is divided into training, validation, and test sets to facilitate…
The Brain, Body, and Behaviour Dataset (Experiment 4) is a multimodal neurophysiological dataset comprising simultaneous recordings of EEG, eye-tracking, cardiac, respiratory, and electrooculographic signals from 43 subjects across two sessions. Participants watched three educational videos (Stim-04, Stim-05, Stim-06) under two attention conditions. In Session 1 (attentive condition), participants viewed the videos and answered comprehension questions afterward. In Session 2 (distracted condition), participants viewed the same three videos in the same order while performing a concurrent backward counting task, with no comprehension testing. This derivative dataset supports investigation of neural and behavioral correlates of attention, learning, and cognitive load during naturalistic video viewing.
…compliant datasets to ensure technical validation and facilitate upload to OpenNeuro. Poetry…
A derivative SSVEP dataset comprising EEG recordings from 8 healthy subjects performing a combined brain-computer interface task with overlapping visual stimuli at a single location. Participants attended to one of four arrow directions (up, down, left, right), each flickering at distinct frequencies (14.17, 12.14, 9.44, 7.73 Hz) derived from an 85 Hz CRT refresh rate. Data were acquired at 1000 Hz using a 32-channel ANT Neuro system across two experimental schemes examining moving dot-formed arrows and space/object-based attention.
…collection, python code for data validation and matlab file for pre-processing…
This dataset comprises longitudinal motor imagery EEG recordings from 18 BCI-naive subjects across six sessions (one offline, five online), designed to study transfer learning and skill acquisition in brain-computer interfaces (BCIs). It compares two domain adaptation frameworks—Generic Recentering and Personally Assisted Recentering—for calibration-free BCI training using left/right hand motor imagery with visual feedback. Data were collected at 512 Hz with 22 EEG channels and analyzed using Riemannian geometry-based classifiers.
…nemar.on006366) # Mouse Sleep Staging Validation dataset (MSSV) This dataset contains EEG…
…The experiment was split into optimization and validation parts. - **Stimulus type**: oddball…