Algorithms of control by thought in robotics: Active and passive BMIs based on prior knowledge

• Published in: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob) pp. 221 - 226
• Main Authors: Chenane, K., Touati, Y., Boubchir, L., Daachi, B., Ali-Cherif, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2016
• Subjects: BCI; BMI; Brain; Control by thought; EEG; Electrodes; Electroencephalography; Mobile robots; SSVEP technique; Training; Visualization
• ISSN: 2155-1782
• DOI: 10.1109/BIOROB.2016.7523627

One of the objectives of the control using the human thought is to make useful robotic systems for persons with high dependency (quadriplegics, paraplegics, etc.). When the human subject is not able to move his limbs, upper or lower, he is no longer able to perform basic and necessary tasks in his daily life. Recently, robotic systems have reached a very advanced level. For example, humanoid robots have become able to walk, recognize and carry objects simultaneously. On the other hand, wearable robots or exoskeletons can help dependent human subject to move and perform tasks previously difficult to imagine. Of course, all these robotic systems cannot perform these tasks except if they are fitted with advanced control schemes. To make these robotic systems, having already some intelligence, more useful, many researchers have studied the problem of controllers based on the user thought. The real challenge is to translate/classify correctly the thought of the user into robotic actions. When the brain activities are not correctly classified or the action thought by the user is not quite performed, it is important to discover it at time. This allows us to update the classifier/controller parameters in order to interpret more precisely the brain activities concerning the following action. This paper deals with looking for relevant prior knowledge that can anticipate any classification error. Thereafter, we propose some reflections regarding the control of robots by passive thought. Our analysis and results are based on the brain machine interface (BMI) using the Steady State Visual Evoked Potentials technique (SSVEP).