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.

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.

Context-based filtering for assisted brain-actuated wheelchair driving.

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased.

Vibrotactile feedback for brain-computer interface operation.

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications.

Preliminary experimentation on vibrotactile feedback in the context of mu-rhythm based BCI.

Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified.

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.