Feature Extraction and Classiﬁcation of Biosignals

Emotion Valence Detection from EEG Signals

A. M. Tom

, A. R. Hidalgo-Mu

noz

, M. M. L

opez

, A. R. Teixeira

, I. M. Santos

, A. T. Pereira

M. V

azquez-Marrufo

and E. W. Lang

DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal

Dept. Experimental Psychology, University of Seville, 41018 Seville, Spain

IEETA, University of Aveiro, 3810-193 Aveiro, Portugal

Dept Ci

encias de Educac¸

ao, University of Aveiro, 3810-193 Aveiro, Portugal

Biophysics, CIML Group, University of Regensburg, 93040 Regensburg, Germany

Keywords:

Valence Detection, Random Forest, ERD/ERS.

Abstract:

In this work a valence recognition system based on electroencephalograms is presented. The performance of

the system is evaluated for two settings: single subjects (intra-subject) and between subjects (inter-subject).

The feature extraction is based on measures of relative energies computed in short time intervals and certain

frequency bands. The feature extraction is performed either on signals averaged over an ensemble of trials or

on single-trial response signals. The subsequent classiﬁcation stage is based on an ensemble classiﬁer, i. e. a

random forest of tree classiﬁers. The classiﬁcation is performed considering the ensemble average responses of

all subjects (inter-subject) or considering the single-trial responses of single subjects (intra-subject). Applying

a proper importance measure of the classiﬁer, feature elimination has been used to identify the most relevant

features of the decision making.

1 INTRODUCTION

During the last decades, information about the emo-

tional state of users has become more and more im-

portant in computer based technologies. Several emo-

tion recognition methods and their applications have

been addressed, including facial recognition, voice

recognition and electrophysiology - based systems

(Calvo and D’Mello, 2010). Concerning the ori-

gin of the signals of the latter systems, they can

be divided into two categories: those originating

from the peripheral nervous system (e.g. heart rate,

Electromyogram - EMG, galvanic skin resistance-

GSR), and those coming from the central nervous sys-

tem (e.g. Electroencephalograms-EEG). Tradition-

ally, EEG-based technology has been used in medical

applications but nowadays it is spreading to other ar-

eas like entertainment and brain-computer interfaces

(BCI). With the emergence of wearable and portable

devices, developing systems based on EEG signals at-

tracted much interest. Therefore, with the availability

of vast amounts of digital data, there is an increasing

interest in the development of machine learning soft-

ware applications. Emotion recognition systems,

dealing with biological signals, exhibit performances

ranging from 40% to 90% depending on the num-

ber of emotion categories of the study. However,

it is not easy to compare them since they differ in

the way emotions are elicited, and in the underlying

model of emotions (e.g. emotional categories). Psy-

chologists represent emotions in a 2D valence/arousal

space (Bradley and Lang, 2007). By dividing the va-

lence (horizontal axis) - arousal (vertical axis) space

into four quadrants, several discrete emotions are usu-

ally identiﬁed (Russell, 1980). The most widely used

categories are the following : Joy (high valence , high

arousal); Pleasure (high valence, low arousal); Anger

(Low valence, High arousal); Sadness (low valence,

low arousal). Some studies include a ﬁfth category

assigned as Neutral which is represented in the region

close to the origin of the 2D valence/arousal space.

Some studies concentrated on one of the dimensions

of the space like identifying the arousal intensity (high

versus low) or the valence (low/ negative versus high

/positive), and eventually a third class neutral state.

Normally, emotions are elicited by (i) presenting an

M. Tomé A., R. Hidalgo-Muñoz A., M. López M., R. Teixeira A., M. Santos I., T. Pereira A., Vázquez-Marrufo M. and W. Lang E..

Feature Extraction and Classiﬁcation of Biosignals - Emotion Valence Detection from EEG Signals.

DOI: 10.5220/0004233100540060

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 54-60

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

external stimulus (picture, sound, word or videos) re-

lated to different emotions at some predeﬁned inter-

val, or by (ii) simply asking subjects to imagine dif-

ferent kinds of emotions. Concerning external visual

stimuli, one may resort to the International Affec-

tive Picture System (IAPS) collection which is freely

available (Lang et al., 2008), and is composed of

pictures classiﬁed by a large number of participants

in terms of Arousal and Valence. The picture set is

widely used in experimental psychology as well as in

automatic emotion recognition systems. Most of the

work in automatic recognition can be considered pi-

lot studies where all experiments are conducted under

laboratory settings where experiments are prepared to

induce emotions. But like in any other classiﬁcation

system, it is needed to establish which signals and

how they are acquired, extract relevant features from

these input signals, and ﬁnally train a classiﬁer. In

this work, a valence recognition system is presented,

the feature extraction module of which is inspired on

brain studies, and its classiﬁcation module is based on

a random forest of decision trees. The latter module

is also applied recursively to achieve feature elimi-

nation. Moreover, the classiﬁcation performance is

measured with respect to inter- and intra-subject clas-

siﬁcation. To achieve such goals, different types of

signals are applied as inputs to the feature extraction

module: either single-trial or ensemble average sig-

nals.

1.1 Classiﬁcation Systems and Emotion

The pioneering work of Picard (Picard et al., 2001) on

affective computing reports a recognition rate of 81%

, achieved by collecting blood pressure, skin conduc-

tance, and respiration information from one person

during several weeks. The subject, an experienced

actor, tried to express eight affective states with the

aid of a computer controlled prompting system (Pi-

card et al., 2001). In (Haag et al., 2004), using the

IAPS data set as stimulus reportoir, peripheral biolog-

ical signals were collected from a single person dur-

ing several days and at different times of the day. By

using a neural network classiﬁer, they consider that

the estimation of the valence value (63.8%) is a much

harder task than the estimation of arousal (89.3%). In

(Kim et al., 2004), a study with 50 participants, aged

from seven to eight years old, is presented. The visual

stimulation with the IAPS data set was considered in-

sufﬁcient, hence they proposed a sophisticated sce-

nario to elicit emotions. It consisted of visual stimuli

using controlled illumination, and additional auditory

stimuli using background music. Simultaneously, an

actress narrated a story (with emotional contents, like

sadness) that was carefully prepared to evoke the sym-

pathy of the subjects. The latter were also requested

to look at a toy in front of them, and it seemed as if

the toy was telling the sad story to the subjects. Only

peripheral biological signals were recorded, and the

measured features were the input of a classiﬁcation

scheme based on a support vector machine (SVM).

The results showed accuracies of 78.4% and 61% for

3 and 4 categories of different emotions, respectively.

In (Schaaff and Schultz, 2009), the data collection

was performed with stimulus pictures taken from the

IAPS repository thus inducing three emotional states

in ﬁve male participants: pleasant, neutral, and un-

pleasant. They obtained, using SVMs, an accuracy of

66.7% for these three classes of emotion, solely based

on features extracted from EEG signals. A similar

strategy was followed by (Macas et al., 2009), where

the EEG data was collected from 23 subjects during

an affective picture stimuli presentation to induce four

emotional states in arousal/valence space. The auto-

matic recognition of the individual emotional states

was performed with a Bayes classiﬁer. The mean ac-

curacy of the individual classiﬁcation was about 75%.

In (Frantzidis et al., 2010), four emotional categories

of the arousal/valence space of the IAPS picture set

were used to elicit emotions of 28 participants and

their EEG signals were recorded. The ensemble aver-

age was computed for each stimulus category and per-

son. Several characteristics (peaks and latencies) as

well as frequency related features (event related syn-

chronization) were measured on a signal ensemble en-

compassing three channels located along the anterior-

posterior line. Then a classiﬁer (a decision tree, C4.5

algorithm) was applied to the set of features to iden-

tify the affective state. An average accuracy of 77.7%

was reported.

1.2 Event Related Potentials and

Emotion

Most of the recognition systems referred above ex-

tract features in segments of the signal deﬁned af-

ter the stimulus presentation. Those features were

found relevant in brain studies and are generally ad-

dressed in studies of event-related potentials (ERP).

ERPs represent transient components in the electroen-

cephalogram (EEG) generated in response to a stim-

ulus, e.g. a visual or auditory stimulus. Studies of

event-related potentials deal with signals that repre-

sent different levels of analysis: signals from single-

trials, ensemble averaged signals where the ensem-

ble encompasses several trials, and signals resulting

from a grand-average over different trials as well as

subjects. The segment of the time series contain-

FeatureExtractionandClassificationofBiosignals-EmotionValenceDetectionfromEEGSignals

ing the single-trial response signal is centered on the

stimulus: t

(negative value) before and t

(positive

value) after stimulus. The ensemble average, over tri-

als of one subject, eliminates the spontaneous activity

of brain maintaining the activity that is phase-locked

with stimulus. And the grand-average is the aver-

age, over participants, of ensemble averages and it is

used mostly to illustrate the outcomes of the study.

In experimental psychology studies, ERP is usually

the ensemble average computed with all single-trials

belonging to one condition (stimulus type). Those

works show that the event-related potentials (ERP)

have characteristics (amplitude and latency) of the

early waves which change according to the nature of

the stimuli (Olofsson et al., 2008). Other investiga-

tions studied the effect of the stimulus in the char-

acteristics frequency bands. Hence, these measures

reﬂect changes in α -, β -, θ - or δ - bands. One

of the most popular, simple and reliable measures

is the event related desychronization/synchronization

(ERD/ERS). It represents a relative decrease or in-

crease in the power content in time - intervals deﬁned

after the stimulus onset when compared to a refer-

ence interval deﬁned before the stimulus onset (Kla-

dos et al., 2009). Usually this measure is computed

for the different characteristic bands of the EEG (Kla-

dos et al., 2009).

2 METHODOLOGY

In this work a valence recognition system is pre-

sented. The performance of the system is evalu-

ated for both single subjects (intra-subject) and be-

tween subjects (inter-subject). The feature extraction

is based on ERD/ERS measures computed in short in-

tervals. The subsequent classiﬁcation stage is based

on an ensemble classiﬁer, i. e. a random forest of tree

classiﬁers. The feature extraction is performed either

on signals averaged over an ensemble of trials or on

single-trial response signals. Accordingly, the clas-

siﬁcation is performed considering the ensemble av-

erage responses of all subjects (inter-subject) or con-

sidering the single-trial responses of single subjects

(intra-subject). Furthermore, feature elimination, ap-

plying a proper importance measure of the classiﬁer,

has been used to identify the most relevant features of

the decision making.

2.1 The Dataset

A total of 26 female volunteers participated in the

study. A total of 21 channels of EEG, positioned

according to the 10 −20 system, and 2 Electroocu-

lograms (EOG) channels (vertical and horizontal)

were sampled at 1kHz and stored. The signals were

recorded while the volunteers were viewing pictures

selected from the IAPS picture repository. A total of

24 high-arousal images, corresponding to an arousal

score s > 6, with positive valences (v = 7.29 ±0.65)

and negative valences (v = 1.47±0.24) were selected.

Each image was presented three times in a pseudo-

random order and each trial lasted 3500ms: during the

ﬁrst 750ms, a ﬁxation cross was presented, then one

of the images was shown during 500ms, and ﬁnally a

black screen followed for a period of 2250ms. The

signals were pre-processed (ﬁltered, eye-movement

corrected, baseline compensated and segmented into

epochs) using the NeuroScan software package. The

single-trial signal length is 950

, with 150

before

the stimulus onset.

2.2 Feature Extraction

The features are extracted from the segmented signals

(either ensemble averaged or single-trial), measuring

the desychronization/synchronization (ERD/ERS) in

four frequency bands. Then the signals are ﬁltered by

four 4th order bandpass Butterworth ﬁlters. The four

characteristic pass-bands are deﬁned as: δ −band :

0.5 −4Hz, θ −band : 4 −7Hz, α −band : 8 −12 Hz

and β −band : 13 − 30Hz. The K = 4 ﬁlters are

applied following a zero-phase forward and reverse

digital ﬁlter methodology not including any transient

(see ﬁltﬁlt MATLAB function (Mathworks, 2012)).

For each ﬁltered signal, the ERD/ERS is estimated in

I = 9 intervals following the stimulus onset and with

a duration of 150ms and 50% of overlap between con-

secutive intervals. The reference interval corresponds

to the 150ms pre-stimulus period. For each interval,

the ERD/ERS is deﬁned as

−E

= 1 −

i = 1, 2 . . . 9; k = 1, . . . 4

where E

represents the energy within the reference

interval, while E

is the energy in the i −th inter-

val after stimulus in the k −th band. Note that when

> E

, the f

is positive otherwise it is negative.

And furthermore notice that the measure has an up-

per bound f

≤ 1 because energy is always a posi-

tive value. In this work, energies E

are computed

by adding up instantaneous energies within each of

the I = 9 intervals of 150ms duration. The energy

is estimated in an interval of 150ms duration de-

ﬁned in the pre-stimulus period. Figure 1 represents

the features computed for two ensemble signals of

channel F7 of one subject. In summary, each va-

lence condition can be characterized by f

ikc

, where

i stands for the time interval, k for the characteristic

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

0 100 200 300 400 500 600 700

−6

−5

−4

−3

−2

−1

time(ms)

ERD/ERS− beta

0 100 200 300 400 500 600 700

−6

−5

−4

−3

−2

−1

timne(ms)

ERD/ERS−alpha

0 100 200 300 400 500 600 700

−1.5

−1

−0.5

0.5

time(ms)

ERD/ERS−theta

0 100 200 300 400 500 600 700

−1.5

−1

−0.5

0.5

time(ms)

ERD/ERS−delta

Figure 1: Features in ensemble average signals of F7 channel of one subject: negative valence (⋄) versus positive valence (+).

band and c for the channel. A total of M = I ×K ×C =

756 features is computed for the multi-

channel segments related with one condition. Follow-

ing, the features f

ikc

will be concatenated into a fea-

ture vector with components f

, m = 1, . . . , M = 756.

2.3 Random Forest

Random forest is an ensemble classiﬁer where train-

ing is based on bootstrapping techniques. The random

forest algorithm, developed by Breiman (Breiman,

2001), is a set of binary decision trees, each perform-

ing a classiﬁcation and the ﬁnal decision is taken by

majority voting. Each tree is grown using a bootstrap

sample from the original data set and each node of the

tree randomly selects a small subset of features for a

split. An optimal split separates the set of samples

of the node into two more homogeneous (pure) sub-

groups with respect to the class of its elements. A

measure for the impurity level is the Gini index. By

considering that ω

, c = 1 . . .C are the labels of the

classes, the Gini index of node i is given by

G(i) = 1 −

∑

c=1

(P(ω

))

where the P(ω

) is the probability of class ω

the set of examples that belong to node i. Note that

G(i) = 0 when node i is pure, e.g, if its data set con-

tains only examples of one class. To perform a split,

one feature f

is tested f

> f

on the set of samples

with n elements which is then divided into two groups

(left and right) with n

and n

elements and the change

in impurity is computed as

△G(i) = G(i) −

(

G(i

) +

G(i

)

The feature and value that results in the largest de-

crease of the Gini index is chosen to perform the split

at node i. Each tree is grown independently using ran-

dom feature selection to decide the splitting test of the

node. The grown trees are not pruned.

The main steps of the algorithm are

1. Given a data set T with N examples, each with

F features. Select the number T of trees, the di-

mension of the subset L < F of features and, the

parameter that controls the size of the tree (it can

be the maximum depth of the tree, the minimum

size of the subset in a node to perform a split).

2. Construct the t = 1 . . . T trees.

(a) Create a training set T

with N examples by

sampling with replacement the original data set.

The out-of-bag data set O

is formed with the

remaining examples of T not belonging to T

(b) Perform the split of node i by testing one of the

L = ⌊

√

F⌋ randomly selected features.

nodes are terminal nodes (leafs) if the number

of examples is n

≤ 0.1N.

3. Repeat step 2 to grow next tree if t ̸= T .

After training, the importance r

of each fea-

ture f

in the ensemble of trees can be computed by

adding the values of △G(i) of all nodes i where the

feature f

is used to perform a split. Sorting the val-

ues r

by decreasing order, it is possible to identify

the relative importance of the features. In this work

T = 500 decision trees were employed.

2.4 Classiﬁcation and Feature

Elimination

In (Guyon et al., 2002) a recursive feature elimination

scheme is proposed based on the values of the param-

eters of the classiﬁer. In this work a similar strategy

using the variable importance r

is applied according

to the following scheme:

1. Initialize: create a set of indices M = {1, 2, . . . M}

relative to the available features and set F ≡ M

2. Organize data set X by forming the feature vectors

with the feature values whose index is in set M

3. Compute the accuracy of the classiﬁer using either

leave-one-out or k-fold cross-validation.

4. Compute the global model of the classiﬁer using

the complete data set X .

5. Compute r

of the features set and eliminate from

set M the indices relative to the twenty least rele-

vant features.

FeatureExtractionandClassificationofBiosignals-EmotionValenceDetectionfromEEGSignals

6. Update the number of features accordingly, i. e.

F ← F −20

7. Repeat steps 2 to 6 while set M is not empty.

The leave-one-out strategy was followed in the

intra-subject experiments and in inter-subject exper-

iments when the ensemble averages were computed

with all available trials of each subject and each con-

dition. The k-fold cross-validation strategy was used

in inter-subject experiments otherwise. Each fold is

formed with the data of each subject, e.g. the classi-

ﬁer is trained with features extracted from 25 subjects

leaving the data of the remaining subject to estimate

the accuracy.

0 100 200 300 400 500 600 700 800

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

# of removed features

accuracy

Figure 2: The inter-subject accuracy versus feature selec-

tion. Features extracted from Ensemble-average signals

with: + with at least 30 single-trials, ⋄ 10 consecutive

single-trials;∗ of 3 consecutive single-trials ;and O single-

trial signal.

Accuracy is the proportion of true results (either

positive or negative valence) in the test set. The leave-

one-out strategy assumes that only one example of the

data set forms the test set while all the remaining be-

long to the training set. But this training and test pro-

cedure is repeated such that all the elements of the

data set are used as test set. Therefore the accuracy is

the proportion of correct decisions taken by classiﬁer

during the execution of the leave-one-out loop strat-

egy. In the intra-subject study the accuracy can be

estimated as the average of the estimates within each

subject.

3 RESULTS AND DISCUSSION

The system was implemented in MATLAB using also

some facilities of open source software tools like

EEGLAB (Delorme and Makeig, 2004) and the ran-

dom forest package (Jaiantilal, 2010). Considering

feature elimination and the concomitant number of

relevant features, as can be seen from ﬁg. 2 and ﬁg.

3, the accuracy of the classiﬁer improves with a de-

creasing number of relevant features in both an inter-

subject or an intra-subject classiﬁcation strategy. In

every case, the accuracy achieves 80% when the clas-

siﬁers have less than 100 relevant features as input.

3.1 Inter-subject Classiﬁcation

Figure 2 shows the accuracy versus the number of fea-

tures eliminated. The accuracy was computed with a

leave-one-out strategy and a total of 52 feature vec-

tors were involved. The highest accuracy is achieved

having as input the ensemble average of all trials. An

average accuracy of 79% is achieved if roughly 500

irrelevant features are removed from the input fea-

ture set. The other traces represent the mean accu-

racy when the ensemble averages are computed with

3, 5, 10 consecutive trials for each condition meaning

that the number of feature vectors for each subject is

roughly 24, 14, 6, respectively. Note that the differ-

ences in accuracy between the various cases consid-

ered might not be statistically signiﬁcant rather ap-

pear as a consequence of the sample size of the test

sets. However notice that the curves follow a similar

trend corroborating the positive effect on the decision

making of eliminating irrelevant features, i. e., dis-

tracting information.

The tables 1 and 2 describe the spatial and tem-

poral location of the relevant selected features when

the input of the classiﬁer is the data set formed by 52

feature vectors. These feature vectors represent the

ensemble average positive and negative response of

all volunteers investigated. Concerning spacial loca-

tions, the largest number of features happens to occur

in the frontal and parietal regions of the brain. Con-

sidering the localization of the response in time, most

of the features display Medium and Long latencies.

These results conﬁrm related brain studies performed

with ensemble average signals (Olofsson et al., 2008).

Table 1: Space Localization of the 36 selected features

within each band: frontal (Fr), central-temporal (CT) and

parietal-occipital (PO).

Channels Beta Alpha Theta Delta Total

Fr. 7 2 4 5 18

CT 6 0 3 0 9

PO 5 2 0 2 9

3.2 Intra-subject Classiﬁcation

Figure 3 shows the mean accuracy when the classiﬁer

is trained with data of one subject. The features were

extracted from single-trial signals as described before.

The training set for each subject is formed by a total

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Table 2: Time Localization of the 36 selected features

within each band. Time intervals: Short (i = {1, 2}),

Medium (i = {3, 4}), Long I (i = {5, 6}) and Long II (i =

{7, 8, 9}).

Time Beta Alpha Theta Delta Total

Short 0 1 0 0 1

Medium 6 1 0 2 9

Long I 12 0 1 3 16

Long II 0 2 6 2 10

0 200 400 600 800

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

# number of removed features

accuracy

Figure 3: Average and standard deviation accuracy of intra-

subjects accuracies versus number of features removed. The

last point corresponds to a removal of 720 features.

of 65 −72 single trials for both classes of emotions.

Again a leave-one-out strategy was employed.

0 1 2 3 4 5 6

100

150

200

250

300

# number of subjects

# number of features

Figure 4: Within the 36 features selected from each indi-

vidual training the histogram counts the number of times a

feature was selected.

A comparison of the outcomes of the individual

training sessions with respect to the features that re-

main after performing the same number of iterations

reveals a large inter-subject variability. Figure 4 dis-

plays this comparison when all individual training

sessions had 36 features as input. It can be seen

that 218 features turn out to be completely irrelevant

and have been eliminated from all classiﬁcation ses-

sions. Another 275 features appear as relevant fea-

tures for the decision making in one of the subjects

under study. Remarkably, only one feature appears

consistently as a relevant feature in at least 6 out of

26 subjects conﬁrming a high inter-subject variability.

A similar conclusion has been drawn in (Macas et al.,

2009) by using a feature selection block before per-

forming classiﬁcation. However notice that a compa-

rable accuracy is achieved whether decision making

is based on a set of 52 feature vectors which represent

ensemble averages over trials and subjects or whether

decision making is based on training classiﬁers indi-

vidually with 65 −72 feature vectors for each subject.

As can be seen in table 3, on average, the most rele-

vant features are appearing again in the frontal region.

Note, however, that this time the parietal-occipital re-

gion seems more relevant than the central-temporal

region.

Table 3: Spatial location versus Frequency: frontal (Fr),

central-temporal (CT) and parietal-occipital (PO). Average

and standard deviation values of number of features within

the 36 selected on each subject.

Ch. Beta Alpha Theta Delta

Fr. 4.6 ±2.9 3.5 ±4.1 2.9 ±2.6 4.0 ±3.1

CT 3.0 ±2.9 2.5 ±2.2 1.2 ±1.6 1.6 ±2.3

PO 2.8 ±2.3 2.8 ±2.7 2.9 ±2.7 4.1 ±3.4

4 CONCLUSIONS

A valence recognition system has been presented and

applied to EEG signals. The latter were recorded from

volunteers subjected to emotional states elicited by vi-

sual stimuli drawn from IAPS repository. The recog-

nition system encompasses a feature extraction stage

and a classiﬁcation module including feature elimina-

tion. A cohort of 26 female volunteers (age 18 −62

years; mean=24.19; std=10.46) has been investigated.

Feature extraction was based on an inter-subject and

an intra-subject methodology. Both methodologies

showed similar performance with regard to the accu-

racy of the random forest classiﬁer. However from

the related Gini index measuring feature importance

no consistent set of features could be identiﬁed sup-

porting the decision making. This points towards a

large biological variability of the set of relevant fea-

tures corresponding to the valence of the emotional

states involved. The classiﬁcation accuracy achieved

compares well with or is even superior to related sys-

tems reported in literature.

Although inter-subject and intra-subject method-

ologies show a similar performance they yet have

different application scenarios. The inter-subject is

mostly suitable for off-line applications like brain

FeatureExtractionandClassificationofBiosignals-EmotionValenceDetectionfromEEGSignals

studies in order to complement the statistical meth-

ods. For instance, in (Hidalgo-Munoz et al., 2012)

an SVM-RFE scheme was applied to identify scalp

spectral dynamics linked with the affective valence

processing. While intra-subject might be interesting

for personalized studies, where subjects need to be

followed over a couple of sessions. Because of the

biologically variability observed intra-subject studies

cannot generalized easily across a cohort of subjects.

ACKNOWLEDGEMENTS

This work is partially funded by FEDER through

the Operational Program Competitiveness Factors -

COMPETE and by National Funds through FCT -

Foundation for Science and Technology in the con-

text of the project FCOMP-01-0124-FEDER-022682

(FCT reference PEst-C/EEI/UI0127/2011). The Fi-

nancial support by the DAAD - FCT is also gratefully

acknowledged.

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