4. Results and discussions
The acquisition of recordings was carried out in the auditory and visual
modalities through electrodes placed at positions Fz, Cz and Pz, as explai-
ned in Section 3.1. In this work, it is reported only the results of the Pz
position in the auditory modality, since it is the region where the ERP sig-
nal generators yield event-related potentials with component more deﬁned
and greater amplitude.
Data matrix Xhas been made up for 16 morphological and 3 spectral
features, and 32 wavelet coeﬃcients. To calculate the wavelet features, the
records must be resampled to 1024 Hz and discrete wavelet transform was
used with a biorthogonal spline as a wavelet function, and 3 vanishing mo-
ments. For this work, a decomposition of 7 levels was applies, in order to
approximately adjust the frequency band levels into the brain rhythms such
as delta (0,2 to 3,5 Hz), theta (3,5 to 7,5 Hz) , alpha (7,5 to 13 Hz) and beta
(13 to 28 Hz). From 7 obtained decomposition levels, approximation coeﬃ-
cients of level 7 and details coeﬃcients of levels 7, 6 and 5 were selected as
characteristic wavelet. To justify the selection of these coeﬃcients was used
a criterion of informativeness based on accumulated Shannon entropy 
with a threshold greater than 60 %.
To carry out the classiﬁcation tasks, it was used three diﬀerent classiﬁers:
ak-NN, a linear discriminant (LDC), and a support vector machine (SVM),
in order to compare the performance of them and select the one that oﬀers
higher performance classiﬁcation. In validation step was used a partition
of 70 % for the training group and 30 % for the test group. The testings
produced the following result:
Figure 4 displays the performance of a k-NN classiﬁer in continuous
repetition to show the stability of the proposed methodology. It can be
observed, that all values of the classiﬁcation performance is above 80 % and
maintain an acceptable standard deviation.
Figure 4 shows the performance obtained by the feature subsets obtained
after selection algorithm SFFS, namely: 1.Performance for the ﬁrst selected
characteristic, 2.Performance for the subset formed by the ﬁrst and second
selected features, and so on.
Table 1 shows the accuracy, speciﬁcity and sensitivity of each group of
features. In table can be seen that from original set of features X, the morp-
hological characteristics are the major contributors in the performance of
the classiﬁer. This same condition is also evident in percentages achieved by
this subset of features for the sensitivity and speciﬁcity.