100 results for "cross-validation" · page 4 of 10 · ranked by relevance
…bandpass=[2.0, 30.0] Hz ## Cross-Validation - **Method**: calibration_vs_online…
This dataset contains EEG recordings from 24 pairs of participants who performed a collaborative 4-way categorisation task based on rules they agreed upon together, along with pre- and post-test sessions using the same stimuli. The study investigates whether collaborative rule learning promotes alignment of neural information between interacting brains, contributing to research on interbrain synchrony and social cognition.
…10-fold cross-validation - **Folds**: 10 - **Evaluation type**: within_session, cross_session…
This dataset comprises 128-channel EEG recordings collected during a speech production paradigm designed to investigate neural correlates of overt, minimally overt, and covert speech. Participants produced one of five color words under each speech condition, with additional peripheral recordings including EOG, EMG, microphone, and display markers to support speech decoding analyses. The dataset includes both calibration sessions for decoder training and online sessions for real-time decoding evaluation.
…CCA ## Cross-Validation - **Evaluation type**: within_subject ## BCI Application - **Applications**: spatial_navigation…
This dataset contains processed intracranial EEG (iEEG) recordings from frontal (LMFG, RMFG) and temporal (LMTG, RMTG) brain regions of 13 epilepsy patients performing a load-3 object working memory task. Data were collected to investigate neural mechanisms underlying working memory maintenance using intracranial electrophysiology. The dataset includes trial-level recordings after artifact rejection, along with behavioral performance data indicating task accuracy.
…SIM, CSP, PCA, CAR, TRCA ## Cross-Validation - **Method**: holdout - **Evaluation type**: within…
Imported from OpenNeuro ds004368
…CAR ## Cross-Validation - **Method**: 2-times repeated 5-fold cross-validation - **Folds…
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.