ArticleComputational intelligence and neuroscience2019
Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry.
Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Decoding lower-limb movement attempts from electro-encephalographic signals in spinal cord injury patients.APL bioengineering · 2026Article
- Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos.Brain sciences · 2025Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies.PloS one · 2024Article
- Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.Frontiers in neuroscience · 2024Article
- Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.Sensors (Basel, Switzerland) · 2023Article
- Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets.Sensors (Basel, Switzerland) · 2023Article
- Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.PloS one · 2022Article
- Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans.PloS one · 2022Article
- A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network.Frontiers in computational neuroscience · 2022Article
- A novel method to reduce the motor imagery BCI illiteracy.Medical & biological engineering & computing · 2021Review
- Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.Sensors (Basel, Switzerland) · 2021Article
- What Internal Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?HCA healthcare journal of medicine · 2021Review
- The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.PeerJ. Computer science · 2021Article
- Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.Frontiers in neurorobotics · 2020Review
- Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation.Frontiers in human neuroscience · 2020Article
- Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.Computational intelligence and neuroscience · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (
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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.