Brain–Computer Interfaces - Index of

178 G.R. Müller-Putz et al.
the imagery task that had to be performed by the patients for about 4 s in each trial.
A minimum of 160 trials (one session) were used to identify suitable frequency
bands with the help of ERD/S maps. Logarithmic band power features were then
calculated from reactive bands and used for LDA classifier setup. The classifier at
time of maximum accuracy was then used for further training.
2.3.1 BCI-Training of Patient TS Using a Neuroprosthesis
with Surface Electrodes
In 1999, patient TS learned to control the cue-based BCI during a very intensive
training period lasting more than 4 months. The training started with left hand vs.
right hand imagination. These MI tasks were changed to to right/left hand vs. idling
or right hand vs. left foot because of insufficient accuracy. Finally, his performance
(session 65) was between 90 and 100 % using right hand and foot motor imaginations
[22]. Because of the prominent ERS during foot MI, the paradigm was changed
to operate asynchronously. Figure 4a presents the average of the LDA output during
an asynchronous neuroprosthetic control experiment. A threshold (TH) was implemented
for the foot class (around 0.5). Whenever the LDA output exceeded this
TH (at second 3) a switch function was triggered [23]. The corresponding features
of the 2 bipolar channels C3 and Cz (α=10–12 Hz, β=15–19 Hz) are shown in
Fig. 4b. It can clearly be seen that the LDA output (Fig. 4a) depends mainly on the
β-band power of channel Cz. This suggested that the system could be simplified by
implementing a TH-comparator of the band power [15].
Fig. 4 (a) Average LDA outputs during grasp phase switching. (b) Corresponding logarithmic
band power features (α and β forC3andCz)toA.(c) Logarithmic β-band power for Cz during
one grasp sequence: hand opens, fingers close (around a drinking glass), hand opens again, and
stimulation stops, so that corresponding muscles relax (modified from [18])