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...

Full description

Saved in:
Bibliographic Details
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
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
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
BookMark eNo1UMtOwzAQNBIcoPQPOFjcU-w4D_tYokIrtaJqy7lynE1jkXgrxyD1j_qZpDxOOzujndndO3Lt0AEhj5xNOGfqaVFsV2CaLFYim8QsTiacSR4LIa7IWOVKipSJTCoub8l51oIJHsEZODa6xYPXxyZ61j1UdK5dRVf4BR24QNc6BPCObsDgwdlg0dEaPV177EMDwRq6wRIDLdANji197607UD30XWmd_hnAmi5xYLcN-hDtwHd0BR36E71kbYM2H0Pw9DNcNsIK_CCHBqv-ntzUuu1h_FdHZPcy2xXzaPn2uiimy8gqHqJc1VlSclVCxfMyT8rhamDAWA0yjQcEWaKUSLkQJuOyipUBzaVIJJNpmYEYkYdfWwsA-6O3nfan_f__xDeSN28h
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICSMech62936.2024.10812333
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798350368918
EndPage 229
ExternalDocumentID 10812333
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i91t-79f64b19bed17b74b233e0e00fe852e0ee649935133c618d29cea18348085b6e3
IEDL.DBID RIE
IngestDate Wed Jan 08 06:10:42 EST 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i91t-79f64b19bed17b74b233e0e00fe852e0ee649935133c618d29cea18348085b6e3
PageCount 5
ParticipantIDs ieee_primary_10812333
PublicationCentury 2000
PublicationDate 2024-Nov.-19
PublicationDateYYYYMMDD 2024-11-19
PublicationDate_xml – month: 11
  year: 2024
  text: 2024-Nov.-19
  day: 19
PublicationDecade 2020
PublicationTitle 2024 IEEE International Conference on Smart Mechatronics (ICSMech)
PublicationTitleAbbrev ICSMECH
PublicationYear 2024
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.8893102
Snippet Electroencephalograph (EEG) signals have expanded beyond the medical field into control systems. Improving EEG-based control technology is crucial to enhancing...
SourceID ieee
SourceType Publisher
StartPage 225
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
URI https://ieeexplore.ieee.org/document/10812333
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFG6UePCkRoy_04PX4sq6dT0qgWAihAAHbqRrXwMHNwLDxP_IP9PXDjQePHjr2mzd2u691-373kfIg8hAGa1T5pxLmHAuYpkTliWphSwSjhtpg9iEHA6z2UyNdmT1wIUBgAA-g5Yvhn_5tjRb_6kM33B0R3EcH5JDKdOarLVLJMoj9fjSmQzALFL0YB580Bat_Qm_pFOC5-id_LPPU9L84eDR0bd3OSMHUJyTz26tWuNbVwu9yzfNntEVWdrXhaWDMiQAr-goJM4s6HiPECoLigGqv-YGgz5cL3Rc5mVFOzVYnQbwANV4_Ia75TBhtHT0tcTayQKjdDZFK04HHpr7QX1fGKiiDbD0aVv5O_KqatjsFak3TTLtdaedPttpLbCl4hWTyqUi5yoHy2UuRY6PDBFEkYMsaWMJUtwaxV4MxqQ8s21lQKM1EBmGbHkK8QVpFGUBl4SCjlxkPF_WcSF1nIERFgNJwxOdSKOuSNOP8nxVZ9OY7wf4-o_6G3Ls59Lz_7i6JY1qvYU7cmTeq-VmfR_WwBcfvrhc
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELWgIMEEiCK-8cDqYjfOh0eoilrRVlXpwFY59lllIKnaFIl_xM_k7LYgBgY2x1bixHbuzsl79wi5lRkoo3XCnHMxk85xljlpWZxYyLh0wqQ2iE2kg0H28qKGa7J64MIAQACfQcMXw798W5ql_1SGbzi6oyiKtslOLGWTr-ha61Sigqu7buu5D2aaoA_z8IOmbGxO-SWeEnzH48E_ez0k9R8WHh1--5cjsgXFMflsr3RrfOtsqtcZp9kDOiNLO7qwtF-GFOAVHYbUmQUdbTBCZUExRPXXXGDYhyuGjsq8rGhrBVenAT5ANR6_4X45TBktHe2VWPs8xTidjdGO074H535Q3xeGqmgFLL1fVv6OvK4aNntN6kWdjB_b41aHrdUW2KsSFUuVS2QuVA5WpHkqc3xk4MC5gyxuYgkS3BxFXg7GJCKzTWVAoz2QGQZteQLRCakVZQGnhILmjhvPmHVCpjrKwEiLoaQRsY5To85I3Y_yZLbKpzHZDPD5H_U3ZK8z7vcmve7g6YLs-3n1bEChLkmtmi_hiuya9-p1Mb8O6-EL2-27ow
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2024+IEEE+International+Conference+on+Smart+Mechatronics+%28ICSMech%29&rft.atitle=Electroencephalograph-Based+Hand+Movement+Pattern+Recognition+for+Prosthetic+Robot+Control+Using+a+Combination+of+Long+Short-Term+Memory+and+Stacked+Autoencoder+Methods&rft.au=Hana+Sasono%2C+Muchamad+Arif&rft.au=Akbar%2C+Afgan+Satrio&rft.au=Fatoni%2C+Moch.+Rijal&rft.au=Nanda+Imron%2C+Arizal+Mujibtamala&rft.date=2024-11-19&rft.pub=IEEE&rft.spage=225&rft.epage=229&rft_id=info:doi/10.1109%2FICSMech62936.2024.10812333&rft.externalDocID=10812333