100 results for "Bayesian model selection" · page 3 of 10 · ranked by relevance
…BrainProducts Cap model: Acticap Electrode type: active Participants ------------ Number of subjects: 12…
Beetl2021-A is a preprocessed motor imagery EEG dataset derived from the BEETL Competition 2021 (NeurIPS Task 2), comprising 63-channel, 500 Hz recordings from healthy subjects performing a four-class motor imagery task (rest, left hand, right hand, feet) during an online BCI racing game (Cybathlon2020IC). The dataset was designed to benchmark transfer learning and domain adaptation methods for subject-independent, cross-dataset EEG-based brain-computer interfacing.
…Task was an MEG-compatible probabilistic selection task. We'll upload their…
[ recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.
…Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for…
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.
This dataset contains raw EEG data from a study investigating real-time, personalized brain state-dependent transcranial magnetic stimulation (TMS) in healthy adults. The research demonstrates that personalized whole-brain activity patterns can predict human corticospinal tract activation in real-time, with potential applications for brain stimulation therapies. Data includes EEG recordings collected during TMS-guided brain state-dependent stimulation protocols, where TMS serves as the intervention guided by real-time EEG decoding.
…EGI - **Cap model**: HydroCel Geodesic Sensor Net (HCGSN) ## Participants - **Number of subjects…
…Processed data and model fits reported in Yuasa et al., (2023) are…