Brain–Computer Interfaces - Index of

Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand 177
of the paralysed and atrophied muscles of the forearm and hand at the Orthopaedic
University Hospital II in Heidelberg starting at January 2000. The Freehand(R) neuroprosthesis
was implanted in September 2000 in his right arm and hand, which was
his dominant hand prior to the injury. After the rehabilitation program, he gained a
substantial functional benefit in performing many activities of everyday life.
2.2 EEG Recording and Signal Processing
In general, the EEG was bipolarly recorded from positions 2.5 cm anterior and posterior
to C3, Cz and C4 (overlying the sensorimotor areas of right hand, left hand
and feet) according the international 10–20 electrode system using gold-electrodes.
In the final experiments, EEG was recorded from the vertex; channel Cz (foot area)
in patient TS, and from positions around Cz and C4 (left hand area) in patient HK.
In both cases, the ground electrode was placed on the forehead. The EEG-signals
were amplified (sensitivity was 50 μV) between 0.5 and 30 Hz with a bipolar EEGamplifier
(Raich, Graz, Austria, and g.tec, Guger Technologies, Graz, Austria),
notch filter (50 Hz) on, and sampled with 125/250 Hz.
Logarithmic band power feature time series were used as input for both experiments.
For identifying reactive frequency bands, time-frequency maps were calculated.
These maps provide data about significant power decrease (event-related
desynchronization, ERD) or increase (event-related synchronization, ERS) in predefined
frequency bands related to a reference period within a frequency range of
interest (for more details see Chapter 3). Usually, these relative power changes are
plotted over the whole trial time and result in so-called ERD/S maps [3].
For both experiments, band power was estimated by band pass filtering
(Butterworth IIR filter with order 5, individual cut-off frequency) of the raw EEG,
squaring and averaging (moving average) samples over a 1-s period. The logarithm
was applied to the band power values, which are generally not normally distributed.
The logarithmic band power features were classified using LDA.
LDA projects features on a line so that samples belonging to the same class form
compact clusters (Fig. 2a). At the same time, the distance between the different
clusters is maximized to enhance discrimination. The weights of the LDA were
calculated for different time points starting at second 0 until the end of the trial
in steps of 0.5 or 0.25 s. Applying a 10-times 10-fold cross validation statistic the
classification accuracy is estimated to avoid over fitting (more details about signal
processing can be found in Chapter 17). The weight vector of the time point with
the best accuracy was then used for further experiments.
2.3 Setup Procedures for BCI Control
As a first step, both patients went through the standard cue-based or synchronous
BCI training to identify reactive frequency bands during hand or foot movement
imagination [24]. This means that cues appearing randomly on a screen indicated