Asynchronous non-invasive brain-actuated control of an intelligent wheelchair.

In this paper we present further results of our asynchronous and non-invasive BMI for the continuous control of an intelligent wheelchair. Three subjects participated in two experiments where they steered the wheelchair spontaneously, without any external cue. To do so the users learn to voluntary modulate EEG oscillatory rhythms by executing three mental tasks (i.e., mental imagery) that are associated to different steering commands. Importantly, we implement shared control techniques between the BMI and the intelligent wheelchair to assist the subject in the driving task. The results show that the three subjects could achieve a significant level of mental control, even if far from optimal, to drive an intelligent wheelchair.

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

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 1h to 2months) 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 non-invasive BCI.

Muscular effort in multiple sclerosis patients during powered wheelchair manoeuvres.

BACKGROUND: This study applied EMG analysis methods to identify muscle group activity profiles and potential overload risks in powered wheelchair use. METHODS: We quantified muscle effort and fatigue using EMG analysis methods during powered wheelchair manoeuvres by 10 multiple sclerosis patients. Video recordings of the different sub-tasks were related to information on surface EMG amplitude (rectified EMG) and spectral information (Median frequency) from M. trapezius, M. deltoideus (pars medius), M. deltoideus (pars anterior), M. pectoralis, M. biceps, M. triceps, wrist extensors and flexors, using Joint Analysis of EMG Spectrum and Amplitude (JASA analysis). FINDINGS: Task durations and subjective data indicated that tasks requiring finer motor control took longer and were perceived as more difficult. Kinesiological functions of all muscle groups identified forward steering to be associated with activation of M. deltoideus (pars anterior), M. pectoralis, M. trapezius and M. deltoideus (pars medius); backwards steering with predominant activation of M. deltoideus (pars medius), M. biceps brachii and wrist flexors; left steering with maximal activation of M. biceps and wrist flexors, and right steering with maximal activation of M. triceps and wrist extensors. These profiles were confirmed in analysis of the functional tasks. JASA analysis documented muscle fatigue in the wrist extensors, whereas increased activation was found in M. trapezius, M. deltoideus (pars anterior) and wrist flexors. INTERPRETATION: EMG based kinesiological analysis gives insight in muscle activity and fatigue during powered wheelchair manoeuvres.