Brain–Computer Interfaces - Index of

294 C. Guger and G. Edlinger
Fig. 10 Typical experimental
workflow for EEG data
acquisition using spatial
pattern calculations (CSP)
without feedback (FB) and
weight vector (WV)
calculation for the sessions
with feedback
CSP1
CSP2
CSP3
Session 1 without FB
Data used to set up CSP1
and WV1
1st day
Session 2 with FB
2nd day
Session 3 without FB
Data used to set up WV2
2nd day
Session 4 with FB
Data used to set up CSP2
and WV3
2nd day
Session 5 with FB
Data used to set up CSP3
and WV4
3rd day
Session 6 with FB
3rd day
WV1
WV2
WV3
WV4
(event-related desynchronization and synchronization) can be calculated to identify
ERD/ERS components during the imagination for further real-time experiments
with feedback [19].
Based on this knowledge e.g. the band power is computed in the alpha and beta
bands of the EEG data. This is done by first band pass filtering the EEG data, then
squaring each sample and averaging over consecutive samples for data smoothing.
This results in a band power estimation in the alpha and in the beta range for each
channel. These signal features are then sent to a classifier that discriminates left from
right movement imagination. As a result, a subject specific weight vector (WV) as
illustrated in Fig. 10 is computed. This weight vector can be used in the next session
to classify in real-time the EEG patterns and to give feedback to the subject as shown
in Fig. 9.
The Simulink model for the real-time analysis of the EEG patterns is shown in
Fig. 11. Here “g.USBamp” represents the device driver reading data into Simulink.
Then the data is converted to “double” precision format and connected to a “Scope”
for raw data visualization and to a “To File” block to store the data in MATLAB
format. Each EEG channel is further connected to 2 “Bandpower” blocks to calculate
the power in the alpha and beta frequency range (both ranges were identified
with the ERD/ERS and spectral analysis). The outputs of the band power calculation
are connected to the “BCI System”, i.e. the real-time LDA implementation which