Brain–Computer Interfaces - Index of

BCIs Based on Signals from Between the Brain and Skull 233
Feedback Experiments
The UM-DBI project has also performed real-time on-line feedback experiments
with epilepsy surgery subjects [28]. Subjects performed one of the self-paced
actions described above while the ECoG averages were viewed in real-time. When
an electrode was found that recorded brain activity related to a particular action, the
initially recorded ECoG was used to setup the feedback system. Under different protocols
[28, 38], feedback training encouraged subjects to change their brain activity
to improve BCI performance. When subjects were given feedback on the signalto-noise
ratio (SNR) [38], three of six subjects showed dramatic improvements in
the SNR of their ECoG, with one subject showing corresponding improvement in
off-line BCI accuracy from 79% hits and 22% false positives to 100% hits and 0%
false positives. When subjects were given feedback on the correlation values used
by the CCTM detection method, [28], one of three subjects showed improvements
in online BCI accuracy from 90% hits with 44% false positives to 90% hits with
10% false positives. Note that on-line accuracy calculations differ due to timing
constraints of the feedback system (see [28]).
fMRI Studies
One of the significant restrictions of current ECoG research is that researchers lack
control over the placement of the electrodes. However, even if it were possible to
choose electrode locations solely for research purposes, we do not yet have an a
priori method for selecting optimal electrode locations. The UM-DBI project is
investigating whether fMRI could be used to select locations for an ECoG-based
BCI [39]. Accurate prediction of electrode location is an important issue, since
implantation of an electrode strip through a burrhole would entail less risk than
opening a large window in the skull to place an electrode grid. Some subjects who
were part of the ECoG studies returned after epilepsy surgery to participate in an
fMRI study while doing the same actions they performed during ECoG recording.
Figure 5 shows the results from a subject who performed a pinch action during
both ECoG and fMRI sessions. BCI detection of this action produced high HFdifferences
over several centimeters of the ECoG grid. Visual comparison of these
areas of good detection with areas that were active on the fMRI during the pinch
action show good general agreement. However, some good ECoG locations do not
correspond to active fMRI areas, and a numerical comparison of individual locations
shows some instances that could be problematic for using fMRI to select ECoG
placement.
6.2.3 The University of Washington in St. Louis
In 2004, Leuthardt et al. reported the application of the frequency analysis
algorithms in BCI2000 [40], developed as part of Wolpaw’s EEG-based work,
to ECoG [4]. Chapter “A Simple, Spectral-change Based, Electrocorticographic