title: Multimodal fusion of muscle and brain signals for a hybrid-BCI.
abstract: Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in the framework of a so called "Hybrid-BCI" (hBCI) approach. Thereby, subjects could achieve a good control of their hBCI independently of their level of muscular fatigue. Furthermore, although EMG alone yields good performance, it is outperformed by the hybrid fusing of EEG and EMG. Two different fusion techniques are explored showing graceful performance degradation in the case of signal attenuation. Such a system allows a very reliable control and a smooth handover if the subjects get exhausted or fatigued during the day.
authors.affiliation: Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, Institute of Bioengineering, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Switzerland. robert.leeb@epfl.ch
authors.forename: Robert
authors.initials: R
authors.lastname: Leeb
authors.forename: Hesam
authors.initials: H
authors.lastname: Sagha
authors.forename: Ricardo
authors.initials: R
authors.lastname: Chavarriaga
authors.forename: Jose
authors.initials: J
authors.lastname: Del R Millan
mesh_terms: D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D004576:Electromyography; D006801:Humans; D008328:Man-Machine Systems; D009132:Muscles / Q000502:physiology*; D013647:Task Performance and Analysis