Brain–Computer Interfaces and Assistive Technology

Abstract

AbstractAssistive technology (AT) supports individuals with motor, sensory, or cognitive disabilities in performing functions that might otherwise be difficult or impossible for them. In particular, individuals with severe motor impairments have a high need for assistive devices supporting access to information technologies, improving mobility, and restoring manipulation abilities. Established human–machine interfaces are dependent on the presence of a sufficient number of residual motor functions. Brain–Computer Interfaces (BCIs) are technical systems that provide a direct connection between the human brain and a computer and can serve as a user interface for the control of assistive devices. Historically, non-invasive BCIs were intended to provide basic communication skills to patients with locked-in syndrome. Since then BCI technology has evolved tremendously and nowadays BCIs are used as an alternative or additional control channel for many other applications. Among them are extended communication applications like accessing the internet or Brain Painting. Wheelchairs and telepresence robots can be navigated with the help of BCIs, and motor-imagery-based BCIs in particular are an attractive perspective for an intuitive neuroprosthesis control. The recent development of the hybrid BCI combining a BCI with other preserved control signals fits well in the user-centered design concept, since BCIs can be seamlessly integrated in traditional AT. Although current non-invasive BCIs are at the stage of entering people’s homes, they still cannot be operated by end-users alone. More home-based studies are needed to further improve the usability and reliability of BCIs and to better address specific needs and requirements of end-users.KeywordsSpinal Cord InjuryAmyotrophic Lateral SclerosisAmyotrophic Lateral Sclerosis PatientAssistive TechnologyFunctional Electrical StimulationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.