Normalized deep learning algorithms based information aggregation functions to classify motor...

ERLNEIL-MDP: Evolutionary reinforcement learning with novelty-driven exploration for medical data processing

J Lv, BG Kim, A Slowik, BD Parameshachari… - Swarm and Evolutionary …, 2024 - Elsevier
The rapid growth of medical data presents opportunities and challenges for healthcare
professionals and researchers. To effectively process and analyze this complex and …
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MCMTNet: Advanced network architectures for EEG-based motor imagery classification

Y Yang, X Zhang, X Zhang, C Yu - Neurocomputing, 2025 - Elsevier
Brain–computer interface (BCI) technology converts electroencephalogram (EEG) signals
into control commands to help patients with motor disorders, such as stroke and amyotrophic …
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Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network

H Cunlin, Y Ye, X Nenggang - Journal of Neural Engineering, 2024 - iopscience.iop.org
Objective. Aiming for the research on the brain–computer interface (BCI), it is crucial to
design a MI-EEG recognition model, possessing a high classification accuracy and strong …
被引用次数：1 相关文章 所有 4 个版本

Hybrid Deep Learning Based PDOA Model for Prediction of BCI Task Using EEG Signals

RK Bhujang, V Kotagi - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Classification of motor imagery using electroencephalography (EEG-MI) is an integral part of
the brain-computer interface (BCI), which helps those with mobility impairments connect with …
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