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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Veröffentlicht in:	2024 IEEE International Conference on Smart Mechatronics (ICSMech) S. 225 - 229
Hauptverfasser:	Hana Sasono, Muchamad Arif, Akbar, Afgan Satrio, Fatoni, Moch. Rijal, Nanda Imron, Arizal Mujibtamala, Anam, Khairul
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 19.11.2024
Schlagworte:	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-Zugang:	Volltext
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