Electroencephalograph-Based Hand Movement Pattern Recognition for Prosthetic Robot Control Using a Combination of Long Short-Term Memory and Stacked Autoencoder Methods

Electroencephalograph (EEG) signals have expanded beyond the medical field into control systems. Improving EEG-based control technology is crucial to enhancing the quality of life for people with disabilities, especially in optimizing prosthetic functions. This research proposes a method to control...

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Bibliographic Details
Published in2024 IEEE International Conference on Smart Mechatronics (ICSMech) pp. 225 - 229
Main Authors Hana Sasono, Muchamad Arif, Akbar, Afgan Satrio, Fatoni, Moch. Rijal, Nanda Imron, Arizal Mujibtamala, Anam, Khairul
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.11.2024
Subjects
Accuracy
Autoencoders
electroencephalograph
Electroencephalography
hand movement pattern recognition
Hands
Long short term memory
Pattern recognition
Reliability
Robot control
robotic prosthetic hand
stacked autoencoder
Testing
Training
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Summary:Electroencephalograph (EEG) signals have expanded beyond the medical field into control systems. Improving EEG-based control technology is crucial to enhancing the quality of life for people with disabilities, especially in optimizing prosthetic functions. This research proposes a method to control a prosthetic hand robot using a combination of Long Short-Term Memory (LSTM) and Stacked Autoencoder (SAE) architecture based on EEG signals. Offline tests were conducted by adjusting various parameters on LSTM and SAE, achieving an average accuracy of 99.89% in single-subject training, indicating strong potential in functional hand motion pattern recognition. However, in cross-subject testing-where the model was tested on subjects other than those used in training-the performance significantly declined, with an average accuracy of 33.97%.
DOI:10.1109/ICSMech62936.2024.10812333