Brain–Computer Interfaces - Index of

102 E.W. Sellers et al.
Fig. 5 Comparison of
regression and classification
for EEG feature translation.
For the two-target case, both
methods require only one
function. For the five-target
case, the regression approach
still requires only a single
function, while the
classification approach
requires four functions. (See
text for full discussion)
function. In contrast, for the five-target case the classification approach requires
that four functions be parameterized. With even more targets, and with variable targets,
the advantage of the regression approach becomes increasingly apparent. For
example, the positioning of icons in a typical mouse-based graphical user interface
would require a bewildering array of classifying functions, while with the regression
approach, two dimensions of cursor movement and a button selection can access
multiple icons, however many there are and wherever they are.
We have conducted preliminary studies that suggest that users are also able to
accurately control a robotic arm in two dimensions just as they control cursor movement
(34]. In another recent study [32] we trained users on a task that emulated
computer mouse control. Multiple targets were presented around the periphery of a
computer screen, with one designated as the correct target. The user’s task was to
use EEG to move a cursor from the center of the screen to the correct target and then
use an additional EEG feature to select the target. If the cursor reached an incorrect
target, the user was instructed not to select it. Users were able to select or reject the
target by performing or withholding hand-grasp imagery [33]. This imagery evokes
a transient EEG response that can be detected. It can serve to improve overall accuracy
by reducing unintended target selections. The results indicate that users could
use brain signals for sequential multidimensional movement and selection. As these
results illustrate, SMR-based BCI operation has the potential to be extended to a
variety of applications, and the control obtained for one task can transfer directly to
another task.
Our current efforts toward improving SMR-based BCI operation are focused
on improving accuracy and reliability and on three-dimensional control [32]. This
depends on identifying and translating EEG features so that the resulting control signals
are as independent, trainable, stable, and predictable as possible. With control
signals possessing these traits, the mutual user and system adaptations are effective,
the required training time is reduced, and overall performance is improved.