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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.


The evolutionof the average velocity during sessions over all five days for subject 1. Thelower line represents the performance when driving without filter, the upperone the average velocity when the filter is active. It is clear that theoverall performance (with and without filter) improves significantly over thecourse of days.
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Related In: Results  -  Collection


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fig14: The evolutionof the average velocity during sessions over all five days for subject 1. Thelower line represents the performance when driving without filter, the upperone the average velocity when the filter is active. It is clear that theoverall performance (with and without filter) improves significantly over thecourse of days.

Mentions: It is also noteworthy to mention that the overallaverage velocity for subject 1 rises over the days as Figure 14 shows,indicating that the subject's driving skills improve gradually. Subject 2 doesnot show a similar evolution (see Figure 15), but in both cases we cansee that the average velocities are much higher when filtering is active. Forsubject 1, the average improvement the filter offers regarding the averagevelocity is 17.58%. For subject 2 the gain is even higher: 22.72%.


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)

The evolutionof the average velocity during sessions over all five days for subject 1. Thelower line represents the performance when driving without filter, the upperone the average velocity when the filter is active. It is clear that theoverall performance (with and without filter) improves significantly over thecourse of days.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig14: The evolutionof the average velocity during sessions over all five days for subject 1. Thelower line represents the performance when driving without filter, the upperone the average velocity when the filter is active. It is clear that theoverall performance (with and without filter) improves significantly over thecourse of days.
Mentions: It is also noteworthy to mention that the overallaverage velocity for subject 1 rises over the days as Figure 14 shows,indicating that the subject's driving skills improve gradually. Subject 2 doesnot show a similar evolution (see Figure 15), but in both cases we cansee that the average velocities are much higher when filtering is active. Forsubject 1, the average improvement the filter offers regarding the averagevelocity is 17.58%. For subject 2 the gain is even higher: 22.72%.

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.