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Context-based filtering for assisted brain-actuated wheelchair driving.

Vanacker G, del R Millán J, Lew E, Ferrez PW, Moles FG, Philips J, Van Brussel H, Nuttin M - Comput Intell Neurosci (2007)

Bottom Line: With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased.Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it.These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

View Article: PubMed Central - PubMed

Affiliation: The Department of Mechanical Engineering, Katholieke Universiteit, 3001 Leuven, Belgium. gerolf.vanacker@mech.kuleuven.be

ABSTRACT
Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

No MeSH data available.


A subjectcontrolling an intelligent wheelchair in a simulated environment. Visible isthe EEG sensor cap with the cables that are connected to the BCI system and thecomputer that runs the shared control system.
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Related In: Results  -  Collection


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fig5: A subjectcontrolling an intelligent wheelchair in a simulated environment. Visible isthe EEG sensor cap with the cables that are connected to the BCI system and thecomputer that runs the shared control system.

Mentions: Experiments were conducted with a commerciallyavailable EEG system feeding the data to the BCI that estimates the user'smental commands. The classifier uses power spectrum information computed fromthe EEG as its input and outputs the estimated probability distribution overthe classes Left , Forward , and Right at a rate of 2 Hz. Asecond computer running the shared control system is attached to the classifiersystem and uses its output to control the wheelchair. In this work, a simulatedenvironment was used (mainly for safety reasons) in which a wheelchair wasmodelled featuring a laser range scanner in front capable of scanning 180degrees (1 scan for each degree) at 5 Hz. The maximum range of this scanner wasfixed to 4.5 m, in accordance with the real physical scanner on our platformSharioto. The wheelchair was placed in the environment shown in Figure 6. Thefigure also shows the two paths the subjects were asked to follow. Figure 5shows a subject during one of the sessions.


Context-based filtering for assisted brain-actuated wheelchair driving.

Vanacker G, del R Millán J, Lew E, Ferrez PW, Moles FG, Philips J, Van Brussel H, Nuttin M - Comput Intell Neurosci (2007)

A subjectcontrolling an intelligent wheelchair in a simulated environment. Visible isthe EEG sensor cap with the cables that are connected to the BCI system and thecomputer that runs the shared control system.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2267887&req=5

fig5: A subjectcontrolling an intelligent wheelchair in a simulated environment. Visible isthe EEG sensor cap with the cables that are connected to the BCI system and thecomputer that runs the shared control system.
Mentions: Experiments were conducted with a commerciallyavailable EEG system feeding the data to the BCI that estimates the user'smental commands. The classifier uses power spectrum information computed fromthe EEG as its input and outputs the estimated probability distribution overthe classes Left , Forward , and Right at a rate of 2 Hz. Asecond computer running the shared control system is attached to the classifiersystem and uses its output to control the wheelchair. In this work, a simulatedenvironment was used (mainly for safety reasons) in which a wheelchair wasmodelled featuring a laser range scanner in front capable of scanning 180degrees (1 scan for each degree) at 5 Hz. The maximum range of this scanner wasfixed to 4.5 m, in accordance with the real physical scanner on our platformSharioto. The wheelchair was placed in the environment shown in Figure 6. Thefigure also shows the two paths the subjects were asked to follow. Figure 5shows a subject during one of the sessions.

Bottom Line: With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased.Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it.These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

View Article: PubMed Central - PubMed

Affiliation: The Department of Mechanical Engineering, Katholieke Universiteit, 3001 Leuven, Belgium. gerolf.vanacker@mech.kuleuven.be

ABSTRACT
Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

No MeSH data available.