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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Published in:2024 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
Language:English
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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Abstract 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%.
AbstractList 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%.
Author Hana Sasono, Muchamad Arif
Akbar, Afgan Satrio
Anam, Khairul
Nanda Imron, Arizal Mujibtamala
Fatoni, Moch. Rijal
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  givenname: Muchamad Arif
  surname: Hana Sasono
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  givenname: Afgan Satrio
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  givenname: Moch. Rijal
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  givenname: Arizal Mujibtamala
  surname: Nanda Imron
  fullname: Nanda Imron, Arizal Mujibtamala
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  organization: University of Jember,Department of Electrical Engineering,Jember,Indonesia
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  givenname: Khairul
  surname: Anam
  fullname: Anam, Khairul
  email: khairul@unej.ac.id
  organization: University of Jember,Department of Electrical Engineering,Jember,Indonesia
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Snippet Electroencephalograph (EEG) signals have expanded beyond the medical field into control systems. Improving EEG-based control technology is crucial to enhancing...
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SubjectTerms 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
Title Electroencephalograph-Based Hand Movement Pattern Recognition for Prosthetic Robot Control Using a Combination of Long Short-Term Memory and Stacked Autoencoder Methods
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