ArticleBrain sciences2020
Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Article
- Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach.Journal of Cancer · 2024Article
- Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces.Frontiers in human neuroscience · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain-Computer Interface inefficiency of subjects.
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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.