Brain–Computer Interfaces - Index of

98 E.W. Sellers et al.
Fig. 1 Three concepts of BCI operation. The arrows through the user and/or the BCI system
indicate which elements adapt in each concept
system. While the machine learning concept appears most applicable to P300-based
BCIs, mutual adaptation is likely to play a role in this system as well, given that
periodically updating the classification coefficients tends to improve classification
accuracy. Others have applied the machine learning concept to SMR control using
an adaptive strategy. In this approach, as the user adapts or changes strategy, the
machine learning algorithm adapts accordingly.
At the Wadsworth Center, one of our primary goals is to develop a BCI that is
suitable for everyday, independent use by people with severe disabilities at home
or elsewhere. Toward that end, over the past 15 years, we have developed a BCI
that allows people, including those who are severely disabled, to move a computer
cursor in one, two, or three dimensions using mu and/or beta rhythms recorded over
sensorimotor cortex. More recently, we have expanded our BCI system to be able to
use the P300 response as originally described by Farwell and Donchin [17].
2 Sensorimotor Rhythm-Based Cursor Control
Sensorimotor rhythms (SMRs) are recorded over central regions of the scalp above
the sensorimotor cortex. They are distinguished by their changes with movement
and sensation. When the user is at rest there are rhythms that occur in the frequency
ranges of 8–12 Hz (mu rhythms) and 18–26 Hz (beta rhythms). When the
user moves a limb these rhythms are reduced in amplitude (i.e., desynchronized).
These SMRs are thus considered to be idling rhythms of sensorimotor cortex that
are desynchronized with activation of the motor system [39] and chapter 3 in this
book). The changes in these rhythms with imagined movement are similar to the
changes with actual movement [28]. Figure 2 shows a spectral analysis of the EEG
recorded over the area representing the right hand during rest and during motor
imagery, and the corresponding waveforms. It illustrates how the EEG is modulated
in a narrow frequency band by movement imagery. Users can employ motor
imagery as an initial strategy to control sensorimotor rhythm amplitude. Since different
imagined movements produce different spatial patterns of desynchronization