<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Many people suffer from movement disabilities and would benefit from an assistive mobility device with practical control. This paper demonstrates a face-machine interface system that uses motion artifacts from electroencephalogram (EEG) signals for mobility enhancement in people with quadriplegia. We employed an Emotiv EPOC X neuroheadset to acquire EEG signals. With the proposed system, we verified the preprocessing approach, feature extraction algorithms, and control modalities. Incorporating eye winks and jaw movements, an average accuracy of 96.9% across four commands was achieved. Moreover, the online control results of a simulated power wheelchair showed high efficiency based on the time condition. The combination of winking and jaw chewing results in a steering time on the same order of magnitude as that of joystick-based control, but still about twice as long. We will further improve the efficiency and implement the proposed face-machine interface system for a real-power wheelchair.</p></abstract>

Many people suffer from movement disabilities and would benefit from an assistive mobility device with practical control. This paper demonstrates a face-machine interface system that uses motion artifacts from electroencephalogram (EEG) signals for mobility enhancement in people with quadriplegia. We employed an Emotiv EPOC X neuroheadset to acquire EEG signals. With the proposed system, we verified the preprocessing approach, feature extraction algorithms, and control modalities. Incorporating eye winks and jaw movements, an average accuracy of 96.9% across four commands was achieved. Moreover, the online control results of a simulated power wheelchair showed high efficiency based on the time condition. The combination of winking and jaw chewing results in a steering time on the same order of magnitude as that of joystick-based control, but still about twice as long. We will further improve the efficiency and implement the proposed face-machine interface system for a real-power wheelchair.

A face-machine interface utilizing EEG artifacts from a neuroheadset for simulated wheelchair control

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

A face-machine interface utilizing EEG artifacts for wheelchair control:

Many people suffer from movement disabilities and would benefit from an assistive mobility device with practical control. This paper demonstrates a...

A face-machine interface utilizing EEG artifacts from a neuroheadset for simulated wheelchair control

Publié en ligne: 28 juil. 2021

Pages: 1 - 10

Reçu: 15 avr. 2021

DOI: https://doi.org/10.21307/ijssis-2021-015

Mots clés
Human–computer interaction, Face-machine interface, Neuroheadset, EEG artifacts, Simulated wheelchair

© 2021 Theerat Saichoo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A face-machine interface utilizing EEG artifacts from a neuroheadset for simulated wheelchair control

Publié en ligne: 28 juil. 2021

Pages: 1 - 10

Reçu: 15 avr. 2021

DOI: https://doi.org/10.21307/ijssis-2021-015

Mots clésHuman–computer interaction, Face-machine interface, Neuroheadset, EEG artifacts, Simulated wheelchair

© 2021 Theerat Saichoo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Human–computer interaction, Face-machine interface, Neuroheadset, EEG artifacts, Simulated wheelchair