Brain-Controlled Wheelchairs: A Robotic Architecture

Abstract

Abstract-Independent mobility is core to being able to perform activities of daily living by oneself. However, powered wheelchairs are not an option for a large number of people who are unable to use conventional interfaces, due to severe motor-disabilities. For some of these poeple, non-invasive braincomputer interfaces (BCIs) offer a promising solution to this interaction problem and in this article we present a shared control architecture that couples the intelligence and desires of the user with the precision of a powered wheelchair. We show how four healthy subjects are able to master control of the wheelchair using an asynchronous motor-imagery based BCI protocol and how this results in a higher overall task performance, compared with alternative synchronous P300-based approaches.

A hybrid BCI for enhanced control of a telepresence robot

“…Although studies have shown that users were able to successfully complete realistic driving tasks without the hybrid approach [9], [17], the extra control channel increases the user's level of authority and independence, without compromising on safety or impacting heavily on the task load.…”

“…One of the most significant challenges currently faced is that in addition to high accuracy in the decoding of mental commands, fast decision-making and split attention are critical [1], [2], [3]. There have been several demonstrations of such braincontrolled devices, ranging from robotic arms [4], [5], to hand orthoses [6], [7]; and from telepresence robots [1], [8], to wheelchairs [9], [10], [11]. These works predominantly take spontaneous approaches, where the subjects learn to voluntarily modulate their sensorimotor brain activity.…”

Brain-Actuated Assistive Mobility for Disabled End-User

“…If no commands were delivered, the default behavior of the robot was activated, which consisted of moving forward and avoiding obstacles with the help of a shared control system using its on-board sensors. The second application was a powered wheelchair equipped with sonars and webcams, whose movements can be controlled similarly to the telepresence robot, except that the subject was co-located with it [4]. The subject had to drive the wheelchair to reach several targets.…”

Review of real brain-controlled wheelchairs

“…Percentage of hits (37) Qualitative evaluation (38) Time required (40) Success rate, time required and transfer rate (commands per minute) (42) Task success, path length, time, used commands, collisions and obstacle clearance (minimum and average distance to the obstacles) (45) Time optimality rate (51) Success rate and error rate (specified in false positives and false negatives) (52) Task success, path length, time required, path length optimality ratio and time optimality ratio (56) Task success, path length, time required, path length optimality ratio and time optimality ratio, collisions, mean velocity, workload, learnability, confidence and difficulty signal (28,46,57). Other papers used methods such as learning vector quantization in mu and beta bands (43), the logarithmic value in the bands of interest (34,35), the common spatial patterns (CSP) (44,59) …”