Information Technology Reference
In-Depth Information
Fig. 7.7
Principle of filter bank common spatial patterns (FBCSP): (1) band-pass filtering the
EEG signals in multiple frequency bands using a filter bank; (2) optimizing CSP spatial filter for
each band; (3) selecting the most relevant filters (both spatial and spectral) using feature selection
on the resulting features
best spectral and spatial filters since each feature corresponds to a single frequency
band and CSP spatial
filter. This algorithm, although simple, has proven to be very
ef
cient in practice. It was indeed the algorithm used in the winning entries of all
EEG data sets from the last BCI competition
2
(Ang et al.
2012
).
7.3.4 Summary for Oscillatory Activity-based BCI
In summary, when designing BCI aiming at recognizing mental states that involve
oscillatory activity, it is important to consider both the spectral and the spatial
information. In order to exploit the spectral information, using band-power features
in relevant frequency bands is an ef
cient approach. Feature selection is also a nice
tool to
find the relevant frequencies. Concerning the spatial information, using or
selecting relevant channels is useful. Spatial
cient solution for
EEG-based BCI in general, and the CSP algorithm is a must-try for BCI based on
oscillatory activity in particular. Moreover, there are several variants of CSP that are
available in order to make it robust to noise, non-stationarity, limited training data
sets, or to jointly optimize spectral and spatial
filtering is a very ef
filters. The next section will address
the EEG signal-processing tools for BCI based on evoked potentials, which are
different from the ones described so far, but share some general concepts.
2
BCI competitions are contests to evaluate the best signal processing and classi
cation algorithms
on given brain signals data sets. See
http://www.bbci.de/competition/
for more info.
