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MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification

IEEE internet of things journal, 2024-09, Vol.11 (17), p.28539-28554

2024

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E‐CNNet: Time‐reassigned Multisynchrosqueezing transform‐based deep learning framework for MI‐BCI task classification

International journal of imaging systems and technology, 2023-07, Vol.33 (4), p.1406-1423

2023

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Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

Journal of neuroengineering and rehabilitation, 2017-05, Vol.14 (1), p.49-49, Article 49

2017

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Correlation-based channel selection and regularized feature optimization for MI-based BCI

Neural networks, 2019-10, Vol.118, p.262-270

2019

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EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

IEEE transactions on biomedical engineering, 2016-01, Vol.63 (1), p.4-14

2016

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An end-to-end deep learning approach to MI-EEG signal classification for BCIs

Expert systems with applications, 2018-12, Vol.114, p.532-542

2018

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Combined action observation and motor imagery therapy: a novel method for post-stroke motor rehabilitation

AIMS neuroscience, 2018-01, Vol.5 (4), p.236-252

2018

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Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks

IEEE transaction on neural networks and learning systems, 2020-10, Vol.31 (10), p.3839-3852

2020

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Unsupervised multi-source domain adaptation via contrastive learning for EEG classification

Expert systems with applications, 2025-02, Vol.261, p.125452, Article 125452

2025

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EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Sensors (Basel, Switzerland), 2019-03, Vol.19 (6), p.1423

2019

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Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification

Neural networks, 2024-11, Vol.179, p.106497, Article 106497

2024

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An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

Biomedical signal processing and control, 2022-04, Vol.74, p.103496, Article 103496

Roy, Arunabha M.

2022

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A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification

IEEE transactions on neural systems and rehabilitation engineering, 2019-10, Vol.27 (10), p.2164-2177

2019

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Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

IEEE transaction on neural networks and learning systems, 2018-11, Vol.29 (11), p.5619-5629

2018

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Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Neural computing & applications, 2023-07, Vol.35 (20), p.14681-14722

2023

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A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

IEEE transactions on instrumentation and measurement, 2021, Vol.70, p.1-9

2021

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Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks

IEEE transaction on neural networks and learning systems, 2022-07, Vol.PP (7), p.1-12

2022

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PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain–computer interface

Neurocomputing (Amsterdam), 2024-08, Vol.593, p.127805, Article 127805

2024

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Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory

IEEE transaction on neural networks and learning systems, 2021-11, Vol.32 (11), p.4814-4825

2021

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MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

IEEE transactions on biomedical engineering, 2022-06, Vol.69 (6), p.2105-2118

2022

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Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

Biomedical signal processing and control, 2017-01, Vol.31, p.398-406

2017

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GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals

IEEE transaction on neural networks and learning systems, 2024-06, Vol.35 (6), p.7312-7323

2024

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Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface

Engineering applications of artificial intelligence, 2022-11, Vol.116, p.105347, Article 105347

Roy, Arunabha M.

2022

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Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

Computers in biology and medicine, 2020-08, Vol.123, p.103843-103843, Article 103843

2020

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How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

Journal of neural engineering, 2022-06, Vol.19 (3), p.31002

2022

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MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification

E‐CNNet: Time‐reassigned Multisynchrosqueezing transform‐based deep learning framework for MI‐BCI task classification

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

Correlation-based channel selection and regularized feature optimization for MI-based BCI

EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

An end-to-end deep learning approach to MI-EEG signal classification for BCIs

Combined action observation and motor imagery therapy: a novel method for post-stroke motor rehabilitation

Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks

Unsupervised multi-source domain adaptation via contrastive learning for EEG classification

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification

An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification

Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks

PRISM: Deep metric learning based personal grouping method to reduce intersubject variability for motor imagery brain–computer interface

Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals

Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface

Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

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