2026-10-13T14:04:12Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33898441 2026-10-10T16:11:48Z category_26299 category_28864 category_24736 item_type_3 month_year_10_2026

Incremental Value of EEG Beyond EMG for Participant-Independent Forecasting of Bilateral Lower-Limb Joint Angles: Reproducibility Package Guangrun Pang (25030619) LEI LIU (23959531) Han-Kyo Seo (25030688) Biomedical engineering not elsewhere classified Artificial intelligence not elsewhere classified Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience) Electroencephalography methods Electromyography -- Evaluation EEG and ET related scripts EMG detectors Gait Analysis Domain Ontology gait prediction lower-limb kinematics Multimodal Fusion Analysis Machine learning in radiotherapy Nested cross-validation (CV) biomechanics of the tendon reproducibility better <p dir="ltr"><b>Reproducibility package for “Incremental Value of EEG Beyond EMG for Participant-Independent Forecasting of Bilateral Lower-Limb Joint Angles.”</b></p><p dir="ltr">This repository contains the code, frozen derived inputs, saved model parameters, out-of-fold predictions, full-precision result tables, figure-generation materials, and verification files supporting a participant-independent analysis of multimodal gait data.</p><p dir="ltr">The study evaluates whether scalp electroencephalography (EEG) provides incremental predictive value beyond electromyography (EMG) for forecasting six bilateral lower-limb joint angles. The analysis includes 57 participants, 163 treadmill trials, and 145,885 matched prediction rows derived from the public PhysioNet Multimodal Gait Dataset. Model evaluation uses participant-disjoint nested cross-validation and participant-level statistical inference.</p><p dir="ltr">The reproducibility package corresponds to analysis/software version 2.0.0 (causal-native). Its primary reproduction entry point is the frozen derived feature and target data. The package includes causal EEG predictors, EMG predictors, participant and fold identifiers, trained model parameters, reference out-of-fold predictions, analysis code, statistical verification materials, and figure-generation resources.</p><p dir="ltr">The archive supports reproduction from the frozen derived inputs. It should not be interpreted as an independently verified cold-start reconstruction of every EEG, EMG, and IMU intermediate from the original raw recordings on a new machine.</p><p dir="ltr">The original source data are available separately from PhysioNet:<br>“A Multimodal Gait Dataset of Brain Activity, Muscle Activity, Kinematics and Ground Forces in Young Adults,” version 1.0.0, DOI: 10.13026/r0ea-7161.</p><p dir="ltr">No raw participant recordings are redistributed in this archive.</p><p dir="ltr"><b>Licensing:</b> Software is provided under the MIT License. Derived data, numerical results, figures, and archive-specific documentation are provided under CC BY 4.0, as specified within the archive.</p><p dir="ltr"><b>Version:</b> 2.0.0 (causal-native)</p> 2026-10-10T16:11:48Z Dataset Dataset 10.6084/m9.figshare.33898441.v2 https://figshare.com/articles/dataset/Incremental_Value_of_EEG_Beyond_EMG_for_Participant-Independent_Forecasting_of_Bilateral_Lower-Limb_Joint_Angles_Reproducibility_Package/33898441 CC BY 4.0