Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
Funder
National Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
https://ieeexplore.ieee.org/ielam/7333/10398877/10416682-aam.pdf
Reference55 articles.
1. Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects
2. A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface
3. Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model
4. A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces
5. An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender
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1. PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain–computer interface;Neurocomputing;2024-08
2. Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024
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