76 results for "Domain Adaptation" · page 1 of 8 · ranked by relevance
…The study compares two domain adaptation frameworks—Generic Recentering (unsupervised) and Personally…
…for evaluating transfer learning and domain adaptation methods across heterogeneous EEG datasets…
…benchmarking of transfer learning and domain adaptation algorithms for motor imagery classification…
[ measurements acquired during a ball-squeeze motor task. The study focuses on optimizing surface-based image reconstruction techniques for high-density fNIRS neuroimaging, with data collected from multiple subjects performing controlled motor activities. The dataset supports methodological research in fNIRS signal processing and brain imaging reconstruction algorithms.
…Similar to ASR but with different generative process - **Domain adaptation**: Cross-user…
A BIDS-converted neuroimaging dataset containing time-series fNIRS data from 18 subjects across two sessions. The dataset includes near-infrared spectroscopy (NIRS) measurements with independent component analysis (ICA) denoising applied. Data were collected using continuous-wave NIRS with simultaneous measurement of oxygenated and deoxygenated hemoglobin concentrations. This dataset is derived from TimeSeriesHbORT_18sub_twoSessionICAdenoise and has been reformatted to comply with BIDS standards. NOTE: This dataset is currently under validation. Critical NIRS metadata fields (SamplingFrequency, NIRSChannelCount, source/detector mapping) and BIDS compliance must be confirmed before public release.
…brain activity correlated to gait adaptation during split-belt treadmill walking. 30…
A multicenter intracranial electroencephalography (iEEG) dataset comprising segmented 3-second single-channel clips from epilepsy patients, annotated for graphoelement classification and artifact detection. The dataset includes 10,000+ clips from multiple subjects with preserved clinical metadata including seizure onset zone (SOZ) flags, electrode anatomy, and reviewer annotations. This resource supports the development and validation of automated signal processing algorithms for clinical neurophysiology applications.