Self-paced brain-controlled wheelchair methodology with shared and automated assistive control

The consistency and reliability of the brain computer interface (BCI) system is often questioned to be safe for controlling a wheelchair as BCIs characteristically experience a low signal-to-noise ratio and low classification accuracy. Electroencephalogram (EEG) acquired non-invasively consists of multiple time-series which are highly correlated because of volume conduction and ambient noises, thus providing a rather blurred image of the brain activity. This low signal-to-noise ratio and low spatial resolution of the data can degrade the translational performance of the BCI. To overcome the low classification accuracy and the uncertainty in commands of the BCI systems, the user has to impart additional concentration and time to navigate the wheelchair to the desired location. This paper presents a brain-controlled wheelchair (BCW) control strategy that reduces the total time required to complete a task and the concentration effort imparted by the user. Two BCW approaches are investigated in this work; a synchronous BCW and a self-paced BCW. These methodologies involve a shared control methodology between the BCI/user component and the automated assistive control (AAC) component. The proposed BCW strategies are compared to state-of-the-art BCW control methodologies available in the literature. The results show that the proposed methods not only reduce the concentration time but also provide a safer and reliable control compared to other BCWs.