![](bg2.png)
Figure 1: Comparison of averaged EEG responses to non-
target stimuli (Xs) and target stimuli (Os). The ensemble
averaging is necessary, otherwise the event-related poten-
tials could hardly be distinguished from noisy EEG signal.
There is a clear P3b component following the Os stimuli.
Negative is plotted upward (Luck, 2005).
1.2. Section 2 presents an experimental off-line BCI
system to evaluate the previously formulated hypoth-
esis. To describe the method, theoretical background
is introduced in Sections 2.1.1 and 2.1.2. Section 2.2
describes the process of data acquisition. The method
for pattern recognition is described in Section 2.3. In
Section 2.4, the proposed method is evaluated. The
paper is concluded in Section 3.
1.1 State of the Art
The P300 speller has been studied extensively and is
one of the well established BCI systems. However,
a recent review of the field (Mak et al., 2011) con-
cludes that more work still needs to be done to opti-
mize the speed and accuracy before the P300 speller is
practical to use with disabled patients. This becomes
even more relevant when considering that paralyzed
patients can display widely varying P300 responses
between subjects. A reliable BCI system must be able
to adapt to the unique ERP responses of each sub-
ject and to handle the variations between trials within
a subject. When using traditional supervised pattern
recognition techniques, it is common to train the BCI
system for each new subject, allowing it to only learn
the characteristics of his/her ERP responses. There-
fore, some approaches might have difficulty if they
use a priori information to make assumptions about
the temporal and spatial characteristics of the standard
P300 response, especially when applied to abnormal
ERPs from paralyzed patients. (Cashero, 2012)
Therefore, the universal BCI system should not
only rely on a priori information about expected
event-related response, but should also be able to
adapt and to provide reasonable accuracy for differ-
ent subjects.
1.2 Using Unsupervised Neural
Networks
In the paper, using unsupervised neural networks
(UNNs), e.g. self-organizing maps, will be explored.
When traditional supervised learning methods are
used, all attention is concentrated on separating the
classes using class labels, and any other information
is ignored by the classifier. Instead of using class
labels from a supervisor, unsupervised neural net-
works learn representation of different kinds of data
types that occur in the data sets. Since no assump-
tions of the class structure of the data are made, the
networks may discover new clusters that have not
been apparent before. Therefore, the method may
also contribute to understand the related feature vec-
tors. Self-organizing maps were successfully applied
to recognition of topographic patterns of EEG spectra
in (Joutsiniemi et al., 1995). Six classes in total were
used, for continuous alpha activity, flat EEG, theta ac-
tivity, eye movements, muscle activity and bad elec-
trodes contact. The authors concluded that SOMs
were able to recognize similar topographic patterns in
different EEGs, also in EEGs not used for the training
of the map. According to (Lotte et al., 2007), Learn-
ing Vector Quantization is the closest approach that
has been investigated regarding P300 BCIs. In (Liang
and Bougrain, 2008), supervised LVQ1 has success-
fully been applied to the P300 data. This further sup-
ports the hypothesis that similar models may be ben-
eficial for P300 BCIs.
Unsupervised ANN, e.g. self-organizing maps
can be trained on the data from a simple odd-ball
experiment. At least two clusters and possibly also
a ”noise” cluster should appear after training. One
cluster is expected to be associated with target fea-
tures, another one with nontarget features and in ad-
dition, the rest will probably be undecidable. An
expert can associate the clusters with classification
classes, or training features with known classes can
be propagated through the network to create the as-
sociations. For each subject, the clusters will be dis-
tributed differently over the map. The percentage of
training features that will be associated with the un-
decidable cluster may indicate to which extent the
subject is suitable for P300 BCIs. The trained neu-
ral network could be applied to a more complex BCI
HEALTHINF2014-InternationalConferenceonHealthInformatics
388