Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand
Meant to improve the overall quality of life for those with physical or motor impairments, this paper explores the use of EEG and its potential in controlling a prosthetic hand. EEG signal acquisition is centered on oscillatory features through the sensory motor rhythm which can be obtained through...
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2022
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ph-ateneo-arc.ecce-faculty-pubs-11302023-02-27T03:45:33Z Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand Limbaga, Neil Joshua Mallari, Kevin Luis Yeung, Nathan Richward Monje, Jose Claro N Meant to improve the overall quality of life for those with physical or motor impairments, this paper explores the use of EEG and its potential in controlling a prosthetic hand. EEG signal acquisition is centered on oscillatory features through the sensory motor rhythm which can be obtained through motor-imagery (MI). The EEGNet, a convolutional neural network, is used for feature extraction and signal classification of five motor-imagery classes of a hand. A reinforced model through a transfer learning approach deemed to have the best cross-validation accuracy. A real-time debugging module for the virtual hand was implemented using MuJoCo HAPTIX. 2022-01-01T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/136 https://doi.org/10.1109/BIBM55620.2022.9995382 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo Brain-computer interface Electroencephalogram Machine learning Neural networks Virtual hand prosthetics Bioelectrical and Neuroengineering Biomedical Biomedical Devices and Instrumentation Biomedical Engineering and Bioengineering Electrical and Computer Engineering Engineering |
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Brain-computer interface Electroencephalogram Machine learning Neural networks Virtual hand prosthetics Bioelectrical and Neuroengineering Biomedical Biomedical Devices and Instrumentation Biomedical Engineering and Bioengineering Electrical and Computer Engineering Engineering |
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Brain-computer interface Electroencephalogram Machine learning Neural networks Virtual hand prosthetics Bioelectrical and Neuroengineering Biomedical Biomedical Devices and Instrumentation Biomedical Engineering and Bioengineering Electrical and Computer Engineering Engineering Limbaga, Neil Joshua Mallari, Kevin Luis Yeung, Nathan Richward Monje, Jose Claro N Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand |
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Meant to improve the overall quality of life for those with physical or motor impairments, this paper explores the use of EEG and its potential in controlling a prosthetic hand. EEG signal acquisition is centered on oscillatory features through the sensory motor rhythm which can be obtained through motor-imagery (MI). The EEGNet, a convolutional neural network, is used for feature extraction and signal classification of five motor-imagery classes of a hand. A reinforced model through a transfer learning approach deemed to have the best cross-validation accuracy. A real-time debugging module for the virtual hand was implemented using MuJoCo HAPTIX. |
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text |
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Limbaga, Neil Joshua Mallari, Kevin Luis Yeung, Nathan Richward Monje, Jose Claro N |
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Limbaga, Neil Joshua Mallari, Kevin Luis Yeung, Nathan Richward Monje, Jose Claro N |
author_sort |
Limbaga, Neil Joshua |
title |
Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand |
title_short |
Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand |
title_full |
Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand |
title_fullStr |
Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand |
title_full_unstemmed |
Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand |
title_sort |
development of an eeg-based brain-controlled system for a virtual prosthetic hand |
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Archīum Ateneo |
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2022 |
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https://archium.ateneo.edu/ecce-faculty-pubs/136 https://doi.org/10.1109/BIBM55620.2022.9995382 |
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