Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

Author:

de Almeida Ribeiro Paulo Rogério123,Lima Brasil Fabricio124ORCID,Witkowski Matthias12,Shiman Farid12,Cipriani Christian5,Vitiello Nicola5ORCID,Carrozza Maria Chiara5,Soekadar Surjo Raphael12ORCID

Affiliation:

1. Institute of Medical Psychology and Behavioral Neurobiology and MEG Center, University of Tübingen, Silcherstraße 5, 72076 Tübingen, Germany

2. Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University of Tübingen, Calwerstraße 14, 72076 Tübingen, Germany

3. International Max Planck Research School for Neural Information Processing, Österbergstraße 3, 72074 Tübingen, Germany

4. International Max Planck Research School for Neural & Behavioral Sciences, Österbergstraße 3, 72074 Tübingen, Germany

5. The BioRobotics Institute, Scuola Superiore Sant’Anna, V.le R. Piaggio 34, 56025 Pontedera, Italy

Abstract

The extent to which humans can interact with machines significantly enhanced through inclusion of speech, gestures, and eye movements. However, these communication channels depend on a functional motor system. As many people suffer from severe damage of the motor system resulting in paralysis and inability to communicate, the development of brain-machine interfaces (BMI) that translate electric or metabolic brain activity into control signals of external devices promises to overcome this dependence. People with complete paralysis can learn to use their brain waves to control prosthetic devices or exoskeletons. However, information transfer rates of currently available noninvasive BMI systems are still very limited and do not allow versatile control and interaction with assistive machines. Thus, using brain waves in combination with other biosignals might significantly enhance the ability of people with a compromised motor system to interact with assistive machines. Here, we give an overview of the current state of assistive, noninvasive BMI research and propose to integrate brain waves and other biosignals for improved control and applicability of assistive machines in paralysis. Beside introducing an example of such a system, potential future developments are being discussed.

Publisher

Hindawi Limited

Subject

Human-Computer Interaction

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