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A Hybrid Brain-computer interface model that integrates EEG and EMG signals as parallel inputs to machine learning models to efficiently classify different upper limb movements for above-elbow amputees.

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Hybrid Brain Computer Interface for Movement Control of Upper Limb Prostheses

This method is adapted by the paper published by Heba Ibrahim Aly, Sherin Youssef and Cherine Fathy to 2018 International Conference on Biomedical Engineering and Applications (ICBEA) held in Portugal.
https://ieeexplore.ieee.org/document/8471729
The proposed hybrid model consists of four phases that includes EEG & EMG data acquisition phase, pre-processing and segmentation phase, feature extraction and selection phase and Classification phase. The Sample code for the experiment is uploaded to the GitHub.

A-Prosthesis-control-can-be-achieved-through-motor-commands-generated-through-EEG

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A Hybrid Brain-computer interface model that integrates EEG and EMG signals as parallel inputs to machine learning models to efficiently classify different upper limb movements for above-elbow amputees.

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