<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...

	Average accuracy (%)
Participants	Proposed modality #1	Proposed modality #2
1	95.8	100
2	95.8	100
3	91.7	95.8
4	87.5	95.8
5	91.7	100
6	87.5	91.7
7	95.8	95.8
8	91.7	95.8
Mean ± SD	92.2 ± 3.46	96.9 ± 2.94

Commands No.	Actions	Output commands
1	Jaw chewing on both sides	Forward
2	Jaw chewing on the left side	Turn Left
3	Jaw chewing on the right side	Turn Right
4	Winking both eyes	Backward
5	Winking the left eye	Turn Left
6	Winking the right eye	Turn Right
Optional	Winking the left eye and then the right eye within 3 sec	Enable/Disable System

if J_L >T_JL & J_R>T_JR,	Decision is “Com#1”
if J_L>J_R & J_L>T_JL,	Decision is “Com#2”
if J_R>J_L & J_R>T_JR,	Decision is “Com#3”
if W_L >T_WL & W_R>T_WR,	Decision is “Com#4”
if W_L >W_R & W_L>T_WL,	Decision is “Com#5”
if W_R>W_L & W_R>T_WR,	Decision is “Com#6”
Otherwise,	No Decision

Sequence No.	Commands	Sequence No.	Commands
1	Turn Left	7	Turn Right
2	Turn Right	8	Turn Left
3	Turn Right	9	Backward
4	Turn Left	10	Turn Left
5	Forward	11	Turn Right
6	Backward	12	Forward

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.

Figure 1:

Figure 2:

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Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Results of the proposed control modalities.

The commands for simulated wheelchair control.

j.ijssis-2021-015.tab.002

The command sequence for testing the performance of the proposed system.

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.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Results of the proposed control modalities.

The commands for simulated wheelchair control.

j.ijssis-2021-015.tab.002

The command sequence for testing the performance of the proposed system.

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