ArticleSensors (Basel, Switzerland)2021
Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification.
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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Comparative Uncertainty Estimation in Neural Network Analysis of Wearable Sensor Signal for Cough and Fall Detection.Sensors (Basel, Switzerland) · 2026Article
- Review
- A voxel-wise uncertainty-guided framework for glioma segmentation using spherical projection-based U-Net and localized refinement.Medical physics · 2026Article
- A process-guided uncertainty-aware deep learning framework for reliable and interpretable industrial fault diagnosis.PloS one · 2026Article
- Scene graph-guided uncertainty decomposition improves confidence calibration in surgical visual question answering.Frontiers in medicine · 2026Article
- Uncertainty-aware genomic classification of Alzheimer's disease: a transformer-based ensemble approach with Monte Carlo dropout.Briefings in bioinformatics · 2025Article
- Enhancing online adaptive radiotherapy with uncertainty based segmentation error and out-of-distribution detection.Frontiers in oncology · 2025Article
- Advancing EEG prediction with deep learning and uncertainty estimation.Brain informatics · 2024Article
- Deep convolutional neural network for weld defect classification in radiographic images.Heliyon · 2024Article
- Article
- Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).Scientific reports · 2023Article
- Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique.Sensors (Basel, Switzerland) · 2023Article
- Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Measuring brain potentials of imagination linked to physiological needs and motivational states.Frontiers in human neuroscience · 2023Article
- Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems.Frontiers in behavioral neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition.
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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.