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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| Vydáno v: | 2024 IEEE International Conference on Smart Mechatronics (ICSMech) s. 225 - 229 |
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IEEE
19.11.2024
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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%. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Muchamad Arif surname: Hana Sasono fullname: Hana Sasono, Muchamad Arif email: arifhana04@gmail.com organization: University of Jember,Department of Electrical Engineering,Jember,Indonesia – sequence: 2 givenname: Afgan Satrio surname: Akbar fullname: Akbar, Afgan Satrio email: afganstrr@gmail.com organization: University of Jember,Department of Electrical Engineering,Jember,Indonesia – sequence: 3 givenname: Moch. Rijal surname: Fatoni fullname: Fatoni, Moch. Rijal email: rijalfatooni@gmail.com organization: University of Jember,Department of Electrical Engineering,Jember,Indonesia – sequence: 4 givenname: Arizal Mujibtamala surname: Nanda Imron fullname: Nanda Imron, Arizal Mujibtamala email: arizal.tamala@unej.ac.id organization: University of Jember,Department of Electrical Engineering,Jember,Indonesia – sequence: 5 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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