C. Results and Discussion
All subjects were able to achieve a remarkably good level of control in the stationary online BCI session, as can be
seen in Table I. Furthermore, the actual driving task was completed successfully by every subject, for every run and
no collisions occurred. A comparison between the typical trajectories followed under the two conditions is shown in
Fig 7. The statistical tests reported in this section are paired Student’s t-tests.
A great advantage that our asynchronous BCI wheelchair brings, compared with alternative approaches like the P300–
based chairs, is that the driver is in continuous control of the wheelchair. This means that not only does the wheelchair
follow natural trajectories, which are determined in real–time by the user (rather than following predefined ones, like in
[5]), but also that the chair spends a large portion of thenavigation time actually moving (see Fig. 8). This is not the
case with some state–of–the–art P300–controlled wheelchairs, where the wheelchair has to spend between 60% and 80% of the manoeuvre time stationary, waiting for input from the user (c.f. Fig. 8 of this article with Fig. 8 of [6]).
In terms of path efficiency, there was no significant difference (p = 0:6107) across subjects between the distance
travelled in the manual benchmark condition (43.18.9 m) and that in the BCI condition (44.94.1 m). Although the actual
environments were different, the complexity of the navigation was comparable to that of the tasks investigated on a P300–
based wheelchair in [6]. In fact, the average distance travelled for our BCI condition (44.94.1 m), was greater than that
in the longest task of [6] (39.31.3 m), yet on average our participants were able to complete the task in 417.6108.1 s,
which was 37% faster than the 659130 s reported in [6]. This increase in speed might (at least partly) be attributed to the
fact that our wheelchair was not stationary for such a large proportion of the trial time.
C. Results and Discussion
All subjects were able to achieve a remarkably good level of control in the stationary online BCI session, as can be
seen in Table I. Furthermore, the actual driving task was completed successfully by every subject, for every run and
no collisions occurred. A comparison between the typical trajectories followed under the two conditions is shown in
Fig 7. The statistical tests reported in this section are paired Student’s t-tests.
A great advantage that our asynchronous BCI wheelchair brings, compared with alternative approaches like the P300–
based chairs, is that the driver is in continuous control of the wheelchair. This means that not only does the wheelchair
follow natural trajectories, which are determined in real–time by the user (rather than following predefined ones, like in
[5]), but also that the chair spends a large portion of thenavigation time actually moving (see Fig. 8). This is not the
case with some state–of–the–art P300–controlled wheelchairs, where the wheelchair has to spend between 60% and 80% of the manoeuvre time stationary, waiting for input from the user (c.f. Fig. 8 of this article with Fig. 8 of [6]).
In terms of path efficiency, there was no significant difference (p = 0:6107) across subjects between the distance
travelled in the manual benchmark condition (43.18.9 m) and that in the BCI condition (44.94.1 m). Although the actual
environments were different, the complexity of the navigation was comparable to that of the tasks investigated on a P300–
based wheelchair in [6]. In fact, the average distance travelled for our BCI condition (44.94.1 m), was greater than that
in the longest task of [6] (39.31.3 m), yet on average our participants were able to complete the task in 417.6108.1 s,
which was 37% faster than the 659130 s reported in [6]. This increase in speed might (at least partly) be attributed to the
fact that our wheelchair was not stationary for such a large proportion of the trial time.
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