Brain–Computer Interfaces - Index of

BCIs in the Laboratory and at Home: The Wadsworth Research Program 101
Fig. 4 Scalp topographies (nose at top) of Pearson’s r values for horizontal (x) and vertical (y)
target positions. In this user, horizontal movement was controlled by a 12-Hz mu rhythm and
vertical movement by a 24-Hz beta rhythm. Horizontal correlation is greater on the right side of
the head, whereas vertical correlation is greater on the left side. The topographies are for R rather
than R 2 to show the opposite (i.e., positive and negative, respectively) correlations of right and left
sides with horizontal target level. Adapted from Wolpaw and McFarland [56]
procedures provide efficient models that generalize to novel target configurations.
Regression provides an efficient method to parameterize the translation algorithm
in an adaptive manner. This method transfers smoothly to different target configurations
during the course of multi-step training protocols. This study clearly
demonstrated strong simultaneous independent control of horizontal and vertical
movement. As documented in the paper [56], this control was comparable in accuracy
and speed to that reported in studies using implanted intracortical electrodes in
monkeys.
EEG-based BCIs have the advantage of being noninvasive. However, it has been
assumed by many that they have a limited capacity for movement control. For example,
Hochberg et al (2006) stated without supporting documentation that EEG-based
BCIs are limited to 2-D control. In fact, we have recently demonstrated simultaneous
EEG-based control of three dimensions of cursor movement [32]. The upper
limits of the control possible with noninvasive recording are unknown at present.
We have also evaluated various regression models for controlling cursor movement
in a four-choice, one-dimensional cursor movement task [33]. We found that
using EEG features from more than one electrode location and more than one
frequency band improved performance (e.g., C4 at 12 Hz and C3 at 24 Hz). In
addition, we evaluated non-linear models with linear regression by including crossproduct
(i.e., interaction) terms in the regression function. While the translation
algorithm could be based on a classifier or a regression function, we have found that
a regression approach is better for the cursor movement task. Figure 5 compares
the classification and regression approaches to selecting targets arranged along a
single dimension. For the two-target case, both the regression approach and the
classification approach require that the parameters of a single function be determined.
For the five-target case, the regression approach still requires only a single