ArticleSensors (Basel, Switzerland)2021
Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Virtual Reality Enhanced Exercise Training in Upper Limb Function of Patients With Stroke: Meta-Analytic Study.Journal of medical Internet research · 2025Pooled it
- Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Article
- Affective Neural Responses Sonified through Labeled Correlation Alignment.Sensors (Basel, Switzerland) · 2023Article
- A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.Sensors (Basel, Switzerland) · 2023Article
- Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.Sensors (Basel, Switzerland) · 2023Article
- IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.Sensors (Basel, Switzerland) · 2023Article
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
- Complex Pearson Correlation Coefficient for EEG Connectivity Analysis.Sensors (Basel, Switzerland) · 2022Article
- An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.