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This paper presents a framework to characterize the emotional processing of Colombian ex-combatants from illegal groups. The classification process is performed using EEG-ERP data and behavioral features from psychological tests. The results show that ex-combatant and civilian populations can be automatically separated using supervised techniques. With this, we can provide a decision support system for psychologists to improve current interventions aimed to help ex-combatants to make a successful reintegration to civilian life.
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Characterization Framework for Ex-combatants Based on EEG and Behavioral
es Quintero-Zea1, Lina M. Sep´
ulveda-Cano2, M´
onica Rodr´
ıguez Calvache1, Sandra Trujillo
Orrego3, Natalia Trujillo Orrego3,4and Jos´
e D. L´
1SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Calle 70 No. 52 - 21, Medell´
ın, Colombia
2ARKADIUS, Engineering Faculty, Universidad de Medell´
ın UDEM, Cra. 87 No. 30 - 65, Medell´
ın, Colombia
3Mental Health Group, School of Public Health, Universidad de Antioquia UDEA, Cra. 53 No. 61-30, Medell´
ın, Colombia
4Neuroscience Group, Faculty of Medicine, Universidad de Antioquia UDEA, Medell´
ın, Colombia
Abstract— This paper presents a framework to characterize
the emotional processing of Colombian ex-combatants from ille-
gal groups. The classification process is performed using EEG-
ERP data and behavioral features from psychological tests. The
results show that ex-combatant and civilian populations can be
automatically separated using supervised techniques. With this,
we can provide a decision support system for psychologists to
improve current interventions aimed to help ex-combatants to
make a successful reintegration to civilian life.
Keywords— Emotional Processing, Emotional Recognition
Task, ERP, Ex-combatants, Supervised Learning.
Ex-combatants from illegal groups in Colombia mani-
fest an increased expression of aggression. A previous work
demonstrated that they present alterations on their emotional
processing [1]. However, this study did not analyze early
stages of physiological processing. Veterans mainly mani-
fest differences on the processing of unpleasant or violent
emotions [2]. On ex-combatants, we hypothesize an atypical
functioning on similar mechanisms. In order to improve the
current interventions, we need biological markers to charac-
terize atypical functioning of emotional processing.
In this line, Electroencephalography (EEG) is widely used
as an index of neurophysiological activity associated to
electrical activations in the brain, measured with electrodes
placed on the scalp [3]. Given that event-related potentials
(ERP) capture neural activity related to both sensory and cog-
nitive processes, it is the most used approach to characterize
EEG changes [4].
Several studies have reported relations between ERP com-
ponents and the behavior or personality of healthy sub-
jects. Results in [4] show an overview of the different ERP
waveforms and the major findings in various psychiatric
conditions. Relations between impulsivity and P3 ampli-
tude/latency were studied in [5] with subjects suffering of
high anxiety. While [2] showed that combat veterans with
PSTD exhibit greater ERPs.
This paper presents a framework based on ERP compo-
nents and psychological tests for automatic characterization
of ex-combatants, in order to design strategies that are useful
for their reintegration into civilian life. For feature selection
and extraction, Partial Least Squares (PLS) method is used as
a supervised projection, aiming to preserve the components
that are maximally related with the labels. Results show that
we indeed can characterize ex-combatants from civilians. The
proposed methodology can be used as a decision support sys-
tem to develop efficient intervention protocols.
A Participants
The participants were 30 Colombian ex-combatants (two
female) from Agencia Colombiana para la Reintegraci´
(ACR) reintegration program, and 20 Colombian individu-
als (paired by sex, age and school level). All subjects partic-
ipated voluntarily and signed an informed consent in agree-
ment with the Helsinki declaration. Demographic informa-
tion is provided in Table 1.
Table 1: Demographic information.
Ex-combatants Civilians
n= 30 n= 20 p
Gender 2:28 2:18 0.678
Age (years) M = 37.50 M = 36.15 0.589
SD = 8.22 SD = 9.17
Educational M = 10.33 M = 11.05 0.373
level (years) SD = 3.10 SD = 2.14
B Emotional Recognition Task Procedure
For this study, we implement a modification of the Dual
Valence Association Task (DVAT) [6]. A two-alternative,
forced-choice task, in which participants are asked to classify
words or faces displayed on a computer screen according to
their valence, into one of two categories (positive or negative)
as quickly as possible [6]. Our modifications consisted in the
inclusion of Neutral as a third valence level, and the removal
of the simultaneous stimuli block described in [6].
Fig. 1 shows the pipeline of a single trial. Block trials are
presented one-by-one with strict alternation between words
and faces, and no more than two consecutive stimuli with the
same valence.
Fig. 1: Experimental design. Trials start with a fixation cross, followed by
a target stimulus: single stimulus face or word. Feedback is provided only in
error trials. Time between trial completion and onset of the subsequent trial
(ISI) varies between 700 and 1000 ms.
C ERP Recordings
EEG signals were sampled at 1000 Hz from a 64-channel
Neuroscan SynAmps2 amplifier. They were band-pass fil-
tered between 0.1 and 30 Hz. We selected 60 electrodes for
this study (HEO, VEO, CB1 and CB2 were excluded as they
do not record neural activity). Acquired EEG signals were
re-referenced off-line to average electrodes, and downsam-
pled to 500 Hz. Continuous EEG data were segmented from
200 ms prior to the stimulus to 800 ms after. All segments
contaminated with eye movement were removed from fur-
ther analysis using Independent Component Analysis (ICA)
and visual inspection. Artifact-free segments were averaged
to obtain ERPs. The preprocessing stage was performed us-
ing EEGLAB Toolbox [7]. Unless DVT presents faces and
words, only word stimuli were considered for this work.
D Psychological Tests
Both ex-combatants and civilians completed a neuropsy-
chological evaluation. It included two psychological tests: the
Social Ability Scale of Gismero (EHS) [8], and the Reactive-
Proactive Aggression Questionnaire (RPQ) [9].
The EHS measures the assertion of individual’s everyday
social interaction. It consists of 33 items, 28 redacted in neg-
ative sense (absence or lack of abilities), and 5 in positive
sense. It explores 6 dimensions of the social skills. This scale
allows obtaining a global score and punctuations for each of
the six dimensions [8].
The theoretical foundation of the RPQ suggests that the
exploration of reactive and proactive aggression has to in-
clude the motives associated with behavior, context and type
of reaction; such as physical and verbal aggression [9]. It has
23 items divided in two dimensions rated as never,sometimes
and often for frequency of occurrence [9].
A Features Extraction
The main ERP components and their properties are sum-
marized in [10]. In accordance with the nature of the DVAT,
two peaks are identified as dominants: (i) N170, a a mem-
ber of the N2 family with latency between 156 and 189 ms,
which reflects expert object recognition; and (ii) P300 (or
P3), which occurs in response to an unexpected stimulus type
approximately 300 ms after the stimulus onset.
In this work, we use the following procedure to identify the
lag and amplitude of the N170 and P300 ERP components:
i) a multilevel 1-D non-decimated Haar wavelet decomposi-
tion is calculated for the ERP signal, ii) the minimum (N170)
and maximum (P300) values of the first approximation coef-
ficient are found between 150 and 350 ms, iii) the occurrence
time of both peaks is brought to the original ERP signal, and
iv) the peak amplitudes are calculated. Fig. 2 shows a ran-
dom ERP signal, its first approximation coefficient, and the
detected peaks.
-200 0 200 400 600 800
Time [ms]
Amplitude [µ v]
Approximation coefficient
Fig. 2: ERP component detection. The peaks N170 and P300 are detected
from the first approximation coefficient of the non-decimated Haar wavelet
decomposition of the ERP. The peaks amplitudes are then obtained from the
original ERP signal.
Finally, we complete the feature vector with the psycho-
logical scores. Specifically, for each participant we take the
global score from the EHS, the punctuations in each dimen-
sion (reactive and proactive) of the RPQ, and the total RPQ
B Partial Least Square Regression
The main idea of PLS regression is to find a linear model
to describe some predicted variables Xin terms of other ob-
servable variables Y. For a Lclassification problem as in [11],
with (xi,yi)X×{C1,...,CL},xRp, with Qthe number of
features and nthe number of observations. The sample vec-
tors Xand response Ymatrices are given by:
x11 . . . x1Q
xn1. . . xnQ
1 0 . . . 0
0 0 . . . 1
where each row of Ycontains ones in positions denoting
class labels. PLS regression searches for a set of components
(called latent vectors) that perform a simultaneous decompo-
sition of Xand Yas in Eq. (2). These components explain as
much as possible of the covariance between Tand U[12].
Here, matrices Eand Fare the error terms, assumed to be
i.i.d. normal, Pand Qare orthogonal loading matrices, and
Tand Uare the projections of Xand Y. The algorithm used
in this work for PLS implementation is the well–know Non-
Linear Iterative Partial Least Squares Algorithm (NIPALS).
C k-Nearest Neighbors Classifier
The k-Nearest Neighbors (kNN) algorithm starts from a
training set Xe=x1,...,xmX,m<n, labeled with a class
label yjY. Its objective is to classify an unknown sample
χ. According to [13], for each xiXethe distance between
χand xicould be calculated as follows:
where δis any distance metric, i.e., Mahalanobis, Euclidean,
Minkowski, Hamming, among others; and wqis the dis-
tance weighting function. The knearest neighbors are se-
lected based on this distance metric. The class of χcould be
assigned to the majority class among the nearest neighbors;
nevertheless, there are several ways to select the χlabel, ac-
cording to the weighting factor.
D Support Vector Machine Classifier
Support vector machines (SVM) are linear classifiers de-
veloped on statistical learning theory (SLT) by Vapnik [14].
SVM are supervised learning models that aim to find an opti-
mal hyperplane to separate the points of two classes. C-SVM
is a widely used type of SVM (see [15] for implementation
For linearly separable data, the maximum margin hyper-
plane is determined by the constrained optimization problem
subject to
where κ(xi,xj)represents a kernel function, and Cis the
penalty factor that controls the complexity of the SVM. The
decision function is
f(x) = sgn m
αiyiκ(xi,x) + b!(5)
For the C-SVM model specification, the Gaussian (or ra-
dial basis) kernel is defined as
κ(x,y) = exp kxyk2
The penalty factor Cand the parameter σof the kernel must
be tuned by minimizing the estimated generalization error.
We used the classification accuracy to tune the parame-
ters of both the number of PLS components, and the number
of neighbors for the kNN classifier. The highest results were
obtained with two components and three neighbors. The C-
SVM model selection was made by grid search. The best per-
formance was reached with C=1.6 and σ=1.0.
Results show that although both classifiers achieved over
75% of accuracy (see Table 2), the SVM-based classifier
presented better performance, achieving 80.00% of accuracy
with a confidence interval of (63.87 - 90.88)%. Despite that
evidence points out the existence of alterations in the emo-
tional processing of ex-combatants, the confidence intervals
are wide due to the limited size of the sample, and because
some of the features found also reflect non-emotional cog-
nitive processes that may be common to both populations.
The sample size of our study was modest because neuropsy-
chological assessment combined with ERPs in illegal ex-
combatants is difficult and uncommon.
Table 2: Accuracy, sensitivity and specificity reached with the proposed
Method Accuracy (%) Sensitivity (%) Specificity (%)
(Conf. Interval) (Conf. Interval) (Conf. Interval)
KNN 77.50 85.00 70.00
(61.14 - 89.03) (69.48 - 94.39) (53.29 - 83.21)
SVM 80.00 85.00 75.00
(63.87 - 90.88) (69.48 - 94.39) (58.48 - 87.14)
Regarding to the population grouping, Fig. 3 shows that
ex-combatants conform a well defined group, while civilians
tend to share some features with the other group, i.e. some
civilian samples mix with the ex-combatants group of sam-
ples. This complicates their identification as a group.
(a) (b)
Fig. 3: Classification results for (a) kNN and (b) SVM. Note that civilians
are spread, while ex-combatants are more compact. This drive to lower
specificity values.
This paper introduced a framework to characterize ex-
combatants from civilian people, as a first step to design in-
terventions to treat their rage issues. This study showed that
supervised techniques with EEG and behavioral features may
differentiate emotional processing from ex-combatants with
high accuracy and sensitivity.
The authors appreciate the assistance of Agencia Colom-
biana para la Reintegraci´
on. This work was partially
supported by Colciencias Grants [122266140116 and
111556933399], CODI-UDEA INV518-16, doctoral fellow-
ship call 647 (year 2014), and research project 762 (Univer-
sidad de Medell´
ın and Neurocentro de Pereira).
The authors declare that they have no conflict of interest.
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Institute: Universidad de Antioquia
Street: Calle 70 No. 52 - 21
City: Medell´
Country: Colombia
... Moreover, this aggressive behavior generally underlies atypical emotional processing (EP) [7]- [10]. EP is crucial for an adequate interpersonal functioning, and it is related to the 1 ...
... A red X letter is shown as feedback in error trials, followed by the described ISI. Further details about the experiment protocol have been previously described [7], [8]. ...
... For each of the four independent EEG frequency bands selected for this analysis: delta (0.1 -4 Hz), theta (4 -7 Hz), alpha (7)(8)(9)(10)(11)(12)(13), and beta (13 -30 Hz); we calculated the spectrum of each data channel. As cross-spectral quantities can be readily computed using multitaper methods [33], we used the multitaper method based on discrete prolate spheroidal sequences as tapers to estimate spectra (see [27] for further details). ...
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Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this work, we propose a SVM-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.
... An important population to investigate this hypothesis is that of ex-combatants. They represent a non-clinical group with well-documented impairments in EP (Tobón et al., 2015;Quintero-Zea et al., 2017;Trujillo et al., 2017). Ex-combatants typically display diminished empathic expressions (McCarroll et al., 2010;Slep et al., 2010), increased levels of aggression and violence (Jakupcak et al., 2007;Taft et al., 2007;Gallaway et al., 2012), high proportion of a wide variety of mental disorders (Taft et al., 2007;Weierstall et al., 2013;Nussio, 2015, 2016) as well as high emotional reactivity reflected via emotion related ERP components (IAPS; Lang et al., 1988;Tobón et al., 2015). ...
... N170 provides a sensitive marker of EP during both semantic (Luo et al., 2010;Ibáñez et al., 2011Ibáñez et al., , 2014Zhang et al., 2014;Chen et al., 2015) and facial processing (Batty and Taylor, 2003;Luo et al., 2010;Meaux et al., 2014). There is evidence that EP forms part of the repository of functions supporting SCB components such as empathy, ToM, and social skills Melloni et al., 2014;Quintero-Zea et al., 2017;Trujillo et al., 2017). Social cognition is known to rely on perceptual integration which subserves the interpretation of human interactions (Andreou et al., 2015) such as inference of emotional stages, intentions, beliefs and reasoning of others (Adolphs, 2001;Petroni et al., 2011;Stanley and Adolphs, 2013;Kalin et al., 2015;Bora et al., 2016). ...
... Social cognition is known to rely on perceptual integration which subserves the interpretation of human interactions (Andreou et al., 2015) such as inference of emotional stages, intentions, beliefs and reasoning of others (Adolphs, 2001;Petroni et al., 2011;Stanley and Adolphs, 2013;Kalin et al., 2015;Bora et al., 2016). For the purpose of the present study, and in line with previous reports (Quintero-Zea et al., 2017;Trujillo et al., 2017), we decided to include three measures of social cognition indexing ToM and empathy [i.e., The Interpersonal Reactivity Index (IRI) (Davis, 1983); the Read the Mind in the Eyes (Baron-Cohen et al., 2001); and the Hinting task (Gil et al., 2012)]. We also included measures of social behaviors which are sensitive to explore ecological patterns of interactions during daily social contacts. ...
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Emotional processing (EP) is crucial for the elaboration and implementation of adaptive social strategies. EP is also necessary for the expression of social cognition and behavior (SCB) patterns. It is well-known that war contexts induce socio-emotional atypical functioning, in particular for those who participate in combats. Thus, ex-combatants represent an ideal non-clinical population to explore EP modulation and to evaluate its relation with SCB. The aim of this study was to explore EP and its relation with SCB dimensions such as empathy, theory of mind and social skills in a sample of 50 subjects, of which 30 were ex-combatants from illegally armed groups in Colombia, and 20 controls without combat experience. We adapted an Emotional Recognition Task for faces and words and synchronized it with electroencephalographic recording. Ex-combatants presented with higher assertion skills and showed more pronounced brain responses to faces than Controls. They did not show the bias toward anger observed in control participants whereby the latter group was more likely to misclassify neutral faces as angry. However, ex-combatants showed an atypical word valence processing. That is, words with different emotions yielded no differences in N170 modulations. SCB variables were successfully predicted by neurocognitive variables. Our results suggest that in ex-combatants the links between EP and SCB functions are reorganized. This may reflect neurocognitive modulations associated to chronic exposure to war experiences.
... Taken together this evidence suggests that EP is crucial for rapidly and accurately scanning the environment in the search of cues that can trigger adaptive social responses. Ex-combatants have shown atypical EP expressions (Boxer et al., 2011;Tobon et al., 2015;Quintero-Zea et al., 2017). They present with an increased reactivity to emotional images (i.e., International Affective Picture System—IAPS) revealed via late electrophysiological responses that are associated to a reduction in their empathic disposition (Tobon et al., 2015). ...
... They present with an increased reactivity to emotional images (i.e., International Affective Picture System—IAPS) revealed via late electrophysiological responses that are associated to a reduction in their empathic disposition (Tobon et al., 2015). A more recent study has confirmed that modulations of Event Related Potentials elicited during the EP of faces or words together with the analysis of aggressive responses and social interactions can distinguish between ex-combatants and controls (Quintero-Zea et al., 2017). Ex-combatants are characterized by persistent aggressive behaviors, reduction of moral standards, the presence of mental health problems, impairments in social interactions (Engen, 2008), and dehumanizing tendencies toward their enemies (Williams et al., 2006). ...
... We recorded reaction time, the number of hits, and the errors. The ERT has been previously used in SCT intervention studies involving adults with high functioning autism (Turner-Brown et al., 2008); schizophrenia (Kurtz and Richardson, 2011), and also in the assessment of Colombian ex-combatants (Quintero-Zea et al., 2017). ...
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Emotional processing (EP) is a complex cognitive function necessary to successfully adjust to social environments where we need to interpret and respond to cues that convey threat or reward signals. Ex-combatants have consistently shown atypical EP as well as poor social interactions. Available reintegration programs aim to facilitate the re-adaptation of ex-combatants to their communities. However, they do not incorporate actions to improve EP and to enhance cognitive-emotional regulation. The present study was aimed at evaluating the usefulness of an intervention focused on Social Cognitive Training (SCT), which was designed to equip ex-combatants enrolled in the Social Reintegration Route with EP and social cognition skills. A group of 31 ex-combatants (mean age of 37.2, 29 men) from Colombian illegal armed groups were recruited into this study. Of these, 16 were invited to take part in a SCT and the other continued with the conventional reintegration intervention. Both groups underwent 12 training sessions in a period 12–14 weeks. They were assessed with a comprehensive protocol which included Psychosocial, Behavioral, and Emotion Processing instruments. The scores on these instruments prior to and after the intervention were compared within and between groups. Both groups were matched at baseline. Ex-combatants receiving the SCT experienced significant improvements in EP and a reduction in aggressive attitudes, effects not observed in those continuing the conventional reintegration intervention. This is the first study that achieves such outcomes in such a population using SCT intervention. We discuss the implications of such results toward better social reintegration strategies.
... Considering that a large part of the sample of this study had combat experience, their role can predispose them to this specific type of aggression. Moreover, literature shows evidence that this type of population commonly has higher proactive aggression scores with respect to population without combat experience (Quintero-Zea et al., 2017). ...
... A recent study in ex-combatants showed a negative correlation between scores on empathy and neurobiological response suggested that, the worse empathy impairments were, the greater the reactivity to emotional saliency observed in the neurobiological response (Tobón et al., 2015). The relation between atypical emotional processing with larger aggressive expression, has been reported recently among ex-combatants (Quintero-Zea et al., 2017). The presence of this combined traits usually tends to be accompanied with an increased social skill profile. ...
How empathic are battle-experienced war veterans and demobilized ex-combatants? Individuals who have participated in war-related violence tend to show an increased risk of mental health problems, which makes their readaptation to postconflict civilian life much more difficult. This study is the first systematic attempt to evaluate whether war experiences are potentially related to empathic deficit among veterans. Based on a sample of 624 demobilized ex-guerrillas and ex-paramilitaries from the Colombian armed conflict, we identify 3 clearly distinct empathic profiles, suggesting that, while lack of empathy is not generalized among ex-combatants, there is an important subgroup of veterans who present such a dispositional profile. Identification of this critical subgroup will be crucial to policies aimed at assisting postconflict reintegration efforts.
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The Colombian armed conflict has affected in some degree its entire population. Health authorities require markers to determine this exposure and provide proper mental-health interventions. Unsupervised learning techniques allow clustering subjects with similar features. Here, we propose a novel methodology to automatically finds the features that best relate to levels of exposure to the armed conflict and associated risks (drug dependency, alcoholism, etc.) through cluster centers. Unlike previous studies on the armed conflict field, we do not use key predefined labels to cluster the data. We test this methodology with a mixed-response type characterization database of 528 features obtained from 346 volunteers with different estimated levels of exposure to extreme experiences in the frame of the Colombian armed conflict. As a result, using the proposed approach we identified 62 features related to exposure. In order to confirm the selected features as violence exposure markers, we created a model based on artificial neural networks (ANN). The ANN model uses the 62 features as input and it was able to estimate the subjects’ level of exposure to conflict with 100 % accuracy in training and over 76% in validation.
Ex-combatants often exhibit atypical Emotional Processing (EP) such as reduced emphatic levels and higher aggressive attitudes. Social Cognitive Training (SCT) addressing socio-emotional components powerfully improve social interaction among Colombian ex-combatants. However, with narrow neural evidence, this study offers a new testimony. A sample of 28 ex-combatants from Colombian illegal armed groups took part of this study, split into 15 for SCT and 13 for the conventional program offered by the Governmental Reintegration Route. All of them were assessed before and after the intervention with a protocol that included an EP task synchronized with electroencephalographic recordings. We drew behavioural scores and brain connectivity (Coherency) metrics from task performance. Behavioural scores yielded no significant effects. Increased post-intervention connectivity in the delta band was observed during negative emotional processing only SCT group. Positive emotions exposed distinctive gamma band connectivity that differentiate groups. These results suggest that SCT can trigger covert neurofunctional reorganization in ex-combatants embarked on the reintegration process even when overt behavioural improvements are not yet apparent. Such covert functional changes may be the neural signature of compensatory mechanisms necessary to reshape behaviours adaptively. This novel framework may inspire cutting-edge transational research at the crossing of neuroscience, sociology, and public policy-making.
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In this work, the neural correlates of emotional processing in Colombian ex-combatants with different empathy profiles were compared to normal controls matched for age, gender and educational level. Forty ex-combatants and 20 non ex-combatants were recruited for this study. Empathy levels as well as executive functions were measured. Empathy level was used to create three groups. Group 1 (G1) included ex-combatants with normal empathy scores, and Group 2 included ex-combatants with low scores on at least one empathy sub-scales. In control group (Ctrl), participants with no antecedents of being combatants and with normal scores in empathy were included. Age, gender, educational and intelligence quotients level were controlled among groups. event-related potentials (ERPs) were recorded while individuals performed an affective picture processing task that included positive, neutral and negative emotional stimuli, which elicit an early modulation of emotion categorization (Early Posterior Negativity (EPN)) and late evaluative process (LPP). EPN differences were found among affective categories, but no group effects were observed at this component. LPP showed a main effect of category and group (higher amplitudes in ex-combatants). There was an inverse correlation between empathy and executive functions scores and ERPs. Results are discussed according to the impact of emotional processing on empathy profile.
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Adults with bipolar disorder (BD) have cognitive impairments that affect face processing and social cognition. However, it remains unknown whether these deficits in euthymic BD have impaired brain markers of emotional processing. We recruited twenty six participants, 13 controls subjects with an equal number of euthymic BD participants. We used an event-related potential (ERP) assessment of a dual valence task (DVT), in which faces (angry and happy), words (pleasant and unpleasant), and face-word simultaneous combinations are presented to test the effects of the stimulus type (face vs word) and valence (positive vs. negative). All participants received clinical, neuropsychological and social cognition evaluations. ERP analysis revealed that both groups showed N170 modulation of stimulus type effects (face > word). BD patients exhibited reduced and enhanced N170 to facial and semantic valence, respectively. The neural source estimation of N170 was a posterior section of the fusiform gyrus (FG), including the face fusiform area (FFA). Neural generators of N170 for faces (FG and FFA) were reduced in BD. In these patients, N170 modulation was associated with social cognition (theory of mind). This is the first report of euthymic BD exhibiting abnormal N170 emotional discrimination associated with theory of mind impairments.
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Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data. This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.
Conference Paper
Generalization performance of support vector machines depends on optimal selection of parameter values. But training the best parameters for C-Support Vector Machines (C-SVM) classifier with RBF kernel is time-consuming. We can hardly finish training process for large data sets with traditional methods. Multithreading as a widespread programming and execution model allows multiple threads to exist within the context of a single process, which has been widely applied in data processing and analyzing. In this paper, we studied how to adopt genetic algorithm and multithreading model to complete optimal model selection of C-SVM classifier with RBF kernel. This new approach not only chooses global parameters, but also saves training time based on parallel computing process. Experimental results show the efficiency and feasibility of new approach.
Posttraumatic stress disorder (PTSD) worsens prognosis following mild traumatic brain injury (mTBI). Combat personnel with histories of mTBI exhibit abnormal activation of distributed brain networks-including emotion processing and default mode networks. How developing PTSD further affects these abnormalities has not been directly examined. We recorded electroencephalography in combat veterans with histories of mTBI, but without active PTSD (mTBI only, n=16) and combat veterans who developed PTSD after mTBI (mTBI+PTSD, n=16)-during the Reading the Mind in the Eyes Test (RMET), a validated test of empathy requiring emotional appraisal of facial features. Task-related event related potentials (ERPs) were identified, decomposed using independent component analysis (ICA) and localized anatomically using dipole modeling. We observed larger emotional face processing ERPs in veterans with mTBI+PTSD, including greater N300 negativity. Furthermore, greater N300 negativity correlated with greater PTSD severity, especially avoidance/numbing and hyperarousal symptom clusters. This correlation was dependent on contributions from the precuneus and posterior cingulate cortex (PCC). Our results support a model where, in combat veterans with histories of mTBI, larger ERPs from over-active posterior-medial cortical areas may be specific to PTSD, and is likely related to negative self-referential activity.
Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
Partial least squares (PLS) regression (a.k.a. projection on latent structures) is a recent technique that combines features from and generalizes principal component analysis (PCA) and multiple linear regression. Its goal is to predict a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal factors called latent variables which have the best predictive power. These latent variables can be used to create displays akin to PCA displays. The quality of the prediction obtained from a PLS regression model is evaluated with cross-validation techniques such as the bootstrap and jackknife. There are two main variants of PLS regression: The most common one separates the roles of dependent and independent variables; the second one—used mostly to analyze brain imaging data—gives the same roles to dependent and independent variables. Copyright © 2010 John Wiley & Sons, Inc. For further resources related to this article, please visit the WIREs website.