Across subjects, it took an average of 160.0 s longer to complete the task under the BCI condition (see Fig. 8,
p = 0:0028). This is probably due to a combination of subjects issuing manual commands with a higher temporal accuracy
and a slight increase in the number of turning commands that were issued when using the BCI (c.f. Fig. 7), which resulted
in a lower average translational velocity. It should be noted that in the manual benchmark condition, the task completion
time varied slightly from subject to subject, as the experiments were carried out on different days, where the changes in
lighting conditions affected the computer vision system. On brighter days, some shadows and reflections from the shiny
wooden floor caused the wheelchair to be cautious and slow down earlier than on dull days, until the sonars confirmed that
actually there was not an obstacle present. Therefore, it makes more sense to do a within subjects comparison, looking at
the performance improvement or degradation on a given day, rather than comparing absolute performance values between
subjects on different days.
From Fig. 8, it can be seen that for the inexperienced users (s1 and s2), there was some discrepancy in the task
completion time between the benchmark manual condition and the BCI condition. However, for the experienced BCI
wheelchair users (s3 and s4), the performance in the BCI condition is much closer to the performance in the manual
benchmark condition. This is likely to be due to the fact that performing a motor–imagery task, whilst navigating and being
seated on a moving wheelchair, is much more demanding than simply moving a cursor on the screen (c.f. the stationary online
BCI session of Table I). In particular, aside from the increased workload, when changing from a task where one has to deliver
a particular command as fast as possible following a cue, to a task that involves navigating asynchronously in a continuous
control paradigm, the timing of delivering commands becomes very important. In order to drive efficiently, the user needs
to develop a good mental model of how the entire system behaves (i.e. the BCI, coupled with the wheelchair) [20].
Clearly, through their own experience, subjects s3 and s4 had developed such mental models and were therefore able to anticipate when they should begin performing a motor imagery task to ensure that the wheelchair would execute the desired
turn at the correct moment. Furthermore, they were also more experienced in refraining from accidentally delivering
commands (intentional non–control) during the periods where they wanted the wheelchair to drive straight forwards and
autonomously avoid any obstacles. Conversely, despite the good online BCI performance of subjects s1 and s2, they
had not developed such good mental models and were less experienced in controlling the precise timing of the delivery of
BCI commands. Despite this, the use of shared control ensured that all subjects, whether experienced or not, could achieve the task safely and at their own pace, enabling continuous mental control over long periods of time (>400 s, almost 7 minutes).