Brain–Computer Interfaces - Index of

100 E.W. Sellers et al.
Fig. 3 Two-dimensional SMR control task with 8 possible target positions. Adapted from Wolpaw
and McFarland [56]. (1) A target is presented on the screen for 1 s. (2) The cursor appears and
moves steadily across the screen with its movement controlled by the user. (3) The cursor reaches
the target. (4) The target flashes for 1.5 s when it is hit by the cursor. If the cursor misses the target,
the screen is blank for 1.5 s. (5) The screen is blank for a 1-s interval prior to the next trial
We use a regression function rather than classification because it is simpler given
multiple targets and generalizes more readily to different target configurations [33].
We use adaptive estimates of the coefficients in the regression functions. The cursor
movement problem is modeled as one of minimizing the squared distance between
the cursor and the target for a given dimension of control. For one-dimensional
movement we use a single regression function. For two- or three-dimensional
movement we use separate functions for each dimension of movement.
We found that a regression approach is well suited to SMR cursor movement control
since it provides continuous control in one or more dimensions and generalizes
well to novel target configurations. The utility of a regression model is illustrated in
the recent study of SMR control of cursor movement in two dimensions by Wolpaw
and McFarland [56]). A sample trial is shown in Fig. 3. Each trial began when a target
appeared at one of eight locations on the periphery of the screen. Target location
was block-randomized (i.e., each occurred once every eight trials). One second later,
the cursor appeared in the middle of the screen and began to move in two dimensions
with its movement controlled by the user’s EEG activity. If the cursor reached
the target within 10 s, the target flashed as a reward. If it failed to reach the target
within 10 s, the cursor and the target simply disappeared. In either case, the screen
was blank for one s, and then the next trial began. Users initially learned cursor
control in one dimension (i.e., horizontal) based on a regression function. Next they
were trained on a second dimension (i.e., vertical) using a different regression function.
Finally the two functions were used simultaneously for full two-dimensional
control. Topographies of Pearson’s r correlation values (a common measure of the
linear relationship between two variables) for one user are shown in Fig. 4. Itis
clear that two distinct patterns of activity controlled cursor movement. Horizontal
movement was controlled by a weighted difference of 12-Hz mu rhythm activity
between the left and right sensorimotor cortex (see Fig. 4, left topography). Vertical
movement was controlled by a weighted sum of activity located over left and right
sensorimotor cortex in the 24-Hz beta rhythm band (see Fig. 4, right topography).
This study illustrated the generalizability of regression functions to varying target
configurations.
This 2004 study also showed that users could move the cursor to novel locations
with equal facility. These results showed that ordinary least-squares regression