Information Technology Reference
In-Depth Information
Fig. 7.8
An example of an
average P300 ERP after a rare
and relevant stimulus (target).
We can clearly observe the
increase in amplitude about
300 ms after the stimulus, as
compared to the non-relevant
stimulus (nontarget)
Averaged ERP waveforms (electrode CZ) for
targets and non targets - S1 - Standing
4
Target
Non target
3
2
1
0
-1
-2
-3
-4
-5
0
0.1
0.2
0.3
0.4
0.5
0.6
Time (s)
7.4
EEG Signal-processing Tools for BCI Based on Event-
related Potentials
An event-related potential (ERP) is a brain responses due to some speci
c stimulus
perceived by the BCI user. A typical ERP used for BCI design is the P300, which is
a positive de
ection of the EEG signal occurring about 300 ms after the user
perceived a rare and relevant stimulus (Fazel-Rezai et al.
2012
) (see also Fig.
7.8
).
ERP are characterized by speci
c temporal variations with respect to the stim-
ulus onset. As such, contrary to BCI based on oscillatory activity, ERP-based BCI
exploit mostly a temporal information, but rarely a spectral one. However, as for
BCI based on oscillatory activity, ERP-based can also bene
t a lot from using the
spatial information. Next section illustrates how the spatial and temporal infor-
mation is used in basic P300-based BCI designs.
7.4.1 Basic Signal-processing Tools for P300-based BCI
In P300-based BCI, the spatial information is typically exploited by focusing
mostly on electrodes located over the parietal lobe (i.e., by extracting features only
for these electrodes), where the P300 is know to originate. As an example, Kru-
sienski et al. recommend to use a set of eight channels, in positions Fz, Cz, P3, Pz,
P4, PO7, Oz, PO8 (see Fig.
7.9
) (Krusienski et al.
2006
).
Once the relevant spatial information identi
ed, here using, for instance, only the
electrodes mentioned above, features can be extracted for the signal of each of
them. For ERP in general, including the P300, the features generally exploit the
temporal information of the signals, i.e., how the amplitude of the EEG signal
varies with time. This is typically achieved by using the values of preprocessed
