Hybrid interface: Integrating BCI in multimodal human-machine interfaces

Emmanuel Kalunga (1, 2) , Sylvain Chevallier (1) , Olivier Rabreau (1) , Eric Monacelli (1)

1 LISV - Laboratoire d'Ingénierie des Systèmes de Versailles
2 TUT - Tshwane University of Technology [Pretoria]

In the context of assistive technologies, it is important to design systems that adapt to the user specificities, and to rely as much as possible on the residual capacities of each user. We define a new methodology in the context of assistive robotics: it is an hybrid approach where a physical interface is complemented by a Brain-Computer Interface (BCI). An implementation of such methodology is proposed, using a 3D touchless interface for continuous control and a steady-state visually evoked potential (SSVEP)-based BCI for triggering specific actions. We describe a novel algorithm for classification of SSVEP signals based on Canonical Correlation Analysis (CCA) and Support Vector Machines (SVM). Its reliability and robustness are assessed in an online setup and its results are compared to existing algorithms. Finally, an experimental evaluation of the proposed system is performed with a 3D navigation task in a Virtual Environment (VE). The system is also embedded on an assistive robotic arm exoskeleton to validate its feasibility.

Domaines: Interface homme-machine [cs.HC]; Robotique [cs.RO]; Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]

Emmanuel Kalunga, Sylvain Chevallier, Olivier Rabreau, Eric Monacelli. Hybrid interface: Integrating BCI in multimodal human-machine interfaces. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Jul 2014, Besançon, France. pp.530 - 535 ⟨10.1109/AIM.2014.6878132⟩. ⟨hal-01352056⟩