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In-Depth Information
Fig. 7.9
Recommended electrodes for P300-based BCI design, according to (Krusienski et al.
2006
)
EEG time points as features. More precisely, features for ERP are generally
extracted by (1) low-pass or band-pass
filtering the signals (e.g., in 1
12 Hz for the
-
P300), ERP being generally slow waves, (2) downsampling the
filtered signals, in
order to reduce the number of EEG time points and thus the dimensionality of the
problem, and (3) gathering the values of the remaining EEG time points from all
considered channels into a feature vector that will be used as input to a classi
er.
This process is illustrated in Fig.
7.10
to extract features from channel Pz for a
P300-based BCI experiment.
Once the features extracted, they can be provided to a classi
er which will be
trained to assigned them to the target class (presence of an ERP) or to the nontarget
class (absence of an ERP). This is often achieved using classical classi
ers such as
LDA or SVM (Lotte et al.
2007
). More recently, automatically regularized LDA
Fig. 7.10
Typical process to extract features from a channel of EEG data for a P300-based BCI
design. On this picture, we can see the P300 becoming more visible with the different processing
steps
