Carlson, T.

Leeb, R.

Millán, JdR

Tonin, L.

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BRAIN-ACTUATED ASSISTIVE MOBILITY FOR DISABLED END-USERLeeb R, Carlson T, Tonin L and Millán JdRChair in Non-Invasive Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Switzerlandrobert.leeb@epfl.chAbstract: Brain-Computer Interfaces (BCIs) have beensuccessfully used to control assistive mobility devices (likea telepresence robot or an electric wheelchair) using onlymotor imagery. Importantly, disabled end-users are able toachieve similar performances as healthy participants.Keywords: Brain-Computer Interfaces, electroencephalo-gram, motor imagery, shared control, robot, wheelchairIntroductionBrain-Computer Interface (BCI) technology enables peopleto control machines not by manual operation, but by mere“thinking”. Thereby, the electrical activity from the brain(the electroencephalogram, EEG) is analyzed in real-timeand control parameters are extracted. BCIs are no longeronly used by healthy subjects under standardized laboratoryconditions, but are also introduced to end-users controllingapplications in their homes [1]. Applications range fromsimple control of computers, by reestablishing communica-tion via writing programs, to motor substitution and games.For people with motor disabilities, regaining mobility is abig issue. In this work, we want to present our latest resultsfrom two assistive mobility devices (a telepresence robotand an electric wheelchair) controlled by disabled end-usersdirectly from a rehabilitation center.MethodsBrain-Computer Interface and signal processing: Allparticipants used motor imagery (e.g. of the left hand, righthand, feet) to control the BCI. Therefore, the brain activitywas acquired via 16 EEG channels over the motor cortex.From the Laplacian filtered EEG, the power spectral den-sity was calculated and classified with a Gaussian classifier(more details see [2]). The output of the BCI is visualizedon a screen and used to control the application.Application prototypes: In case of the telepresence appli-cation, the subject remotely controlled the robot, steering itto the left or to the right within an office environment [3].If no commands were delivered, the default behavior of therobot was activated, which consisted of moving forward andavoiding obstacles with the help of a shared control systemusing its on-board sensors.The second application was a powered wheelchair equippedwith sonars and webcams, whose movements can be con-trolled similarly to the telepresence robot, except that thesubject was co-located with it [4]. The subject had to drivethe wheelchair to reach several targets.Both applications were quite demanding for the subjects,since besides the increased workload and the split atten-tion [2], a certain temporal precision was required.Results and DiscussionUp to now, 9 end-users (aged 37.7±12.2 year) tested thetelepresence application. They achieved a ratio between thetime needed to reach the targets with manual control vs.BCI control of 0.87 ± 0.09 (whereby 1 would correspondto perfect and 0 to no control). These performances aresimilar to healthy participants [3]. In case of the wheelchaironly one person tested it and achieved an amazing 0.97.Most importantly we could demonstrate that all end-userswho achieved good BCI performance could also control theapplications successfully with the help of the shared con-trol system. This is very important, because having a goodBCI control does not guarantee good control over the ap-plication, due to the necessary split attention between theapplication and the BCI, and the temporal constraints [2].In the future we have to add a start/stop or a pausing func-tionality for the movement of the robotic device. In theframework of a hybrid BCI, such commands could also bedelivered through other channels such as residual muscu-lar activity, which can be controlled reliably—but not veryoften, because of the quick fatigue.AcknowledgementThis work was supported by the European ICT ProgrammeProject TOBI FP7-224631.Bibliography[1] J. d. R. Millán, R. Rupp, et. al., “Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges,” Front Neurosci, vol. 4,p. 161, 2010.[2] R. Leeb, S. Perdikis, et. al., “Transferring brain-computer interface skills: from simple bci training tosuccessful application control,” Artif Intell Med, 2013,in revision.[3] L. Tonin, T. Carlson, et. al., “Brain-controlled telep-resence robot by motor-disabled people,” in Conf ProcIEEE Eng Med Biol Soc, pp. 4227–4230, 2011.[4] T. Carlson and J. d. R. Millán, “Brain–controlledwheelchairs: A robotic architecture,” IEEE Robot Au-tom Mag, vol. 20, no. 1, pp. 65–73, 2013.Biomed Tech 2013; 58 (Suppl. 1) © 2013 by Walter de Gruyter · Berlin · Boston. DOI 10.1515/bmt-2013-4437

Brain-Actuated Assistive Mobility for Disabled End-User

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