A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots

Abstract

Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. Methods: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1 h to 2 months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. Results: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. Conclusions: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. Significance: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and noninvasive BCI.

The auditory P300-based single-switch brain–computer interface: Paradigm transition from healthy subjects to minimally conscious patients

“…In contrast, a brain-computer interface (BCI) is a technology that utilizes neurophysiological signals directly from the brain to control external devices, bypassing the natural muscular output [1]. Currently, BCI systems based on electroencephalography (EEG) can provide severely motordisabled people with a new output channel to voluntarily control applications for communication and environmental control [2,3,4,5,6,7,8].…”

A hybrid BCI for enhanced control of a telepresence robot

“…The eventual aim is to empower people with severe motordisabilities to (re-)gain a degree of independence. 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].…”

“…Nevertheless, complex robotic devices have been successfully and reliably controlled by such BCIs, by exploiting smart interaction designs, such as shared control [12], [13], [14], [15]. Millán's group has pioneered the use of shared control in neuroprosthetics, by taking the continuous estimation of the user's intentions and providing appropriate assistance to execute tasks safely and reliably [1], [2], [16]. Furthermore, thanks to the mutual learning approach, where the user and the BCI are coupled together and adapt to each other, end-users are able to learn to operate brain-actuated devices relatively quickly (typically in a matter of hours spread across few days [8], [3], [17], [18]).…”

Modelling user attention for human-agent interaction

“…Another approach consists of identifying brain activation patterns associated to functional tasks, such as walking or moving an arm, to translate them into computer commands. This approach has the potential to provide communication mechanisms to control interactions in virtual environments [3] or wheelchair control for individuals with a degree of paralyses [4].…”