\section{Brain-Computer Interfaces}\label{BCI}
Brain-Computer Interfaces (BCIs) were first developed in the 1970s, though advancements in this field have been quite recent (Millán, 2003). Wolpaw et al (2002) have reviewed BCI research and explained its main components to be signal acquisition, signal processing, the output device and the operating protocol. Brain signals are acquired using EEG recording, either non-invasively from the scalp or invasively using electrodes implanted within the brain to pick up cortical neuronal activity. Both evoked and spontaneous potentials have been used by researchers. Evoked potentials are generated in response to external stimuli, while spontaneous potentials are due to intentional mental activity such as imagining limb movement, performing mental arithmetic operations etc. (Millán, 2003). Signal processing involves feature extraction, i.e., identifying the signals of interest which may contain the user's messages, and translating these signals into one of the several commands that can be issued by the user. In the case of a BCI-controlled intelligent wheelchair, the output device is the wheelchair and the user commands are hence used to navigate the wheelchair. The operating protocol refers to whether the communication is continuous or discontinuous, synchronous (brain signals are recorded in association with trigger signals) or asynchronous (user is free to generate commands at any time), method of turning on and off the system etc.

The Strathclyde BCI developed in the Neurophysiology Laboratory at the University of Strathclyde captures brain signals non-invasively using 14 EEG channels. The signals acquired in response to visual directional cues are classified and used to control a wheelchair on-line in a virtual reality simulator (Valsan et al, 2009). \hyperref[simulator]{Chapter 3} provides further details of the simulator and wheelchair control.

In order to fully utilise the potential of brain-computer interfaces, the user must develop the skill to produce useful EEG signals (Wolpaw, 2002). Hence extensive training is required prior to operating wheelchairs controlled by BCIs. In order to compensate for the lack of accuracy of signals, off-loading some level of control to the wheelchair would be favourable both in terms of safety and workload of the user. Moreover, the number of commands available to manoeuvre the wheelchair is limited - four directional commands in the Strathclyde BCI (Grychtol, 2010). Hence fine-steering requires great skill which may not be possible for many people with cognitive disabilities. In this case too, a certain degree of wheelchair autonomy would be advantageous. Therefore, shared control of the wheelchair by both the user and an intelligent wheelchair controller would be beneficial (Millán, 2009).