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to characterize and quantify those variations. The corresponding features include
Hjorth parameters (Obermeier et al.
2001
) or time domain parameters (TDP)
(Vidaurre et al.
2009
). Recent research results have even suggested that TDP
could be more ef
cient that the gold-standard band-power features (Vidaurre
et al.
2009
; Ofner et al.
2011
).
Connectivity measures: They measure how much the signal from two channels
are correlated, synchronized or even if one signal may be the cause of the other
one. In other words, connectivity features measure how the signal of two
channels are related. This is particularly useful for BCI since it is known that, in
the brain, there are many long distance communications between separated areas
(Varela et al.
2001
). As such, connectivity features are increasingly used for BCI
and seem to be a very valuable complement to traditional features. Connectivity
features include coherence, phase locking values or directed transfer function
(DFT) (Krusienski et al.
2012
; Grosse-Wentrup
2009
; Gouy-Pailler et al.
2007
;
Caramia et al.
2014
).
Complexity measures: They naturally measure how complex the EEG signal
may be, i.e., they measure its regularity or how predictable it can be. This has
also been shown to provide information about the mental state of the user and
also proved to provide complementary information to classical features such as
band-power features. The features from this category used in BCI include
approximate entropy (Balli and Palaniappan
2010
), predictive complexity
(Brodu et al.
2012
) or waveform length (Lotte
2012
).
Chaos theory-inspired measures: Another category of features that has been
explored is chaos-related measures, which assess how chaotic the EEG signal
can be, or which chaotic properties it can have. This has also been shown to
extract relevant information. Examples of corresponding features include fractal
dimension (Boostani and Moradi
2004
) or multi-fractal cumulants (Brodu et al.
2012
).
cient as the standards
tools such as band-power features, they usually extract a complementary infor-
mation. Consequently, using band-power features together with some of these
alternative features has led to increase classi
While these various alternative features may not be as ef
cation performances, higher that the
performances obtained with any of these features used alone (Dornhege et al.
2004
;
Brodu et al.
2012
; Lotte
2012
).
It is also important to realize that while several spatial
filters have been designed
for BCI, they are optimized for a speci
c type of feature. For instance, CSP is the
optimal spatial
filter for band-power features and xDAWN or Fisher spatial
filters
are optimal spatial
filters for EEG time points features. However, using such spatial
filters with other features, e.g., with the alternative features described above, would
be clearly suboptimal. Designing and using spatial
filters dedicated to these alter-
native features are therefore necessary. Results with waveform length features
indeed suggested that dedicated spatial
filters for each feature signi
cantly improve
classi
cation performances (Lotte
2012
).
