Analysis of Brain Waves in Violent Images

Are Differences in Gender?

Juan Andrés Martínez-Escobar, Silvia B. González-Brambila and Josué Figueroa-González

Systems Department, Universidad Autonoma Metropolitana, San Pablo Av. 180, Mexico City, Mexico

Keywords: Violence Images, Data Mining, EEG, Gender.

Abstract: We collected information using the Electroencephalograph (EEG) EmotivEpoc, and the software complement

of the Eye Tracking system SMI RED250mobile. As a first step, it was stored in text files, the readings of

each EEG sensor during the time the presentation of 5 violent images and 5 non-violent images were observed.

The database was collected with 50 volunteers, consisting of 25 men and 25 women. The database was later

loaded into R, for the execution of the algorithms of data mining, K-means, K-medoids, Hierarchical

Clustering, Naive Bayes, Support Vector Machines, Adaboost and Decision trees. In the clustering methods,

a random clustering was presented and with little information, with the Naive Bayes, SVM and Adaboost

models, a classification with a high percentage of error was obtained using the Decision Trees method, we

obtained one of the worst results, with the highest error rates in the classification performed with the test data

of selected method. Based on the results obtained, no significant difference was found in the individual's

gender, which affected his reaction when viewing images with violent and non-violent content.

1 INTRODUCTION

Humans analyze and react in different ways when see

or observe different situations; however, it is

desirable to identify patterns that allow classification.

Due to the easy access to Internet, people can

access to a lot of information, but this privilege has

brought with it a great danger, graphic violent content

may be unfit or disturbing for many people. As a

result, several works have been done related with the

classification of videos and images into violent and

non-violent content under different criteria, which has

been a topic of interest and research in recent years.

In this project we analyzed the brain waves of

people when they witnessed images that could be

classified as violent or non-violent, in order to

compare the results obtained, for determining if there

are differences or not in the gender.

First we acquire brainwave data of people

observing the violent and non-violent images using

the EmotivEpoc, this data was stored in a database.

Then, this data was schematized using the software R,

and processed using some data mining algorithms

using packages of the same software. The goal was to

classify the samples in at least two groups, Male and

Female samples. The EmotivEpoc is a wireless EEG

of 14 channels, designed for research and advanced

brain computer interface.

There are some related works about violence in

images and videos; recognizing acts of violence on

videos with crowds, without audio (Hassner, 2012).

Classifying images in violent and non-violent using

the BoW model integrated with the SPM scheme and

soft voting strategy (Wang, 2012). There area also

some works related with the consequences of

watching violence on TV (Tisserom, 2006), and

recording with an EEG how young men react while

they do a laboratory test called Taylor Aggression

Paradigm (Wiswede, 2011). A recent work

(Manrique, 2014) used some data mining algorithms

to classify the sound of firearms shots.

(Lotte, 2007) focus on the classification

algorithms used to design EEG-based Brain-

Computer Interface (BCI) and the used features, they

aware that problems may be different if used outside

the laboratories. The classification algorithms used to

design BCI systems were divided into five categories:

a) Linear classifiers (LDA, Linear Discriminant

Analysis and SVM, Support Vector Machine), b)

Neural Networks (MLP, MultiLayer Perceptron and

other architectures), c) NonLinear Bayesian

classifiers (Bayes quadratic and HMM, Hidden

Markov Model), d) Nearest Neighbor classifiers

408

Martínez-Escobar, J., González-Brambila, S. and Figueroa-González, J.

Analysis of Brain Waves in Violent Images - Are Differences in Gender?.

DOI: 10.5220/0006485304080414

In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 408-414

ISBN: 978-989-758-255-4

(kNN and Mahalanobis distance) and e) combinations

of classifiers. They conclude that SVM are

particularly efficient for synchronous BCI and

combinations of classifiers and dynamic classifiers

also result very efficient in synchronous experiments.

The present work pretends to be an antecedent of

future studies on repercussions to the mind of the

human being when observing violent content,

depending if the person observing it is of the feminine

or masculine gender. The goal, carrying out a study

on the cerebral activity that generates the viewing of

images classified as violent or non-violent, is trying

to detect whether is a difference in gender or not.

For the statistic analysis and data mining prcess,

was used the R software. R is a GNU project that has

a wide variety of statistical and graphical techniques

and is extensible. It is a complete computer language

and allow additional funcionality so it can be

extended via packages (R, 2017).

The general diagram is presented in Figure 1.

Figure 1: Components diagram of the project.

This paper is organized as follows, in section 2 we

present the process for collecting the information,

which includes the construction of the database,

presentation of images and the collection of EEG

data. In section 3, we present the analysis and the

interpretation of results obtained from each

algorithm. Finally, in section 4, we expose our

conclusion and comment about future related works.

2 GATHERING INFORMATION

2.1 Building the Image Database

There is no database available with classified images

in violence and no violence, so we had to make one,

by searching and collecting images through the online

search engine “Google”, using query words such as

“violence”, “horror”, “explosion”, “blood”, “shots”,

“war”, and similar, to those used in the construction

of the VID database (Wang, 2012). After creating the

database, 10 images were selected, 5 classified as

violent and 5 non-violent, by personal criteria of a

group of undergraduate and graduate students. For the

presentation that would be shown to the volunteers of

the project, these images must had a minimum

resolution of approximately 1000x800 and

1200x1000 pixels. These images must maintain a

certain parallelism of the contents, as for example:

people, scenes, objects, etc.

2.2 Image Presentation

Violent and non-violent images were presented with

the help of the Eye Tracking system, 50 volunteers

from the university community, 25 from male gender

and 25 from female gender. First we had to place the

EEGdevice, which requires that the electrodes were

sufficiently hydrated with saline solution for the

correct measurement, so that some potential

volunteers, could not perform the test due to hair

products that blocked or didn’t allowed the electrodes

to contact the skin.

2.3 Collecting the EEG Data

After having placed the data channels and obtained a

good signal of the EmotivEpoc device, we proceeded

to the present the violent and non-violent images in a

monitor.

Each image was exposed for three-second to each

volunteer, that duration was selected by suggestion of

M.D. Roberto García, whom from personal

experience of previous works, pointed out that it

didn’t require more time for the image to cause a

reaction in the observer.

After collecting the EEG data of the 50

volunteers, the EEG database was extracted with the

SMI Experiment Suite software in the Laboratory,

which also allowed us to observe the path of the

volunteer’s vision during the exposure to the picture.

The database information was processed to a text

file, separated by tabulations, which contains: name

of the volunteer, name of the image that was observed

at a moment, values registered from the 14 electrodes,

information of the gyroscope and emotion values that

were automatically calculated by the EEG device,

with an average performance of 60 values per second

registered by the EmotivEpoc diadem.

Analysis of Brain Waves in Violent Images - Are Differences in Gender?

409

3 ANALYSIS AND

INTERPRETATION OF

RESULTS

Due to the large amount of data obtained through the

EmotivEpoc equipment, since several values per

second were extracted in each of the electrodes, we

proceeded to form tables, as objects of R, where for

each volunteer their maximum, mean, median and

variance values of each EEG channel were showed

during the exposure of each of the violent and non-

violent images.

It should be mentioned that in the creation of

tables, a column was added, indicating if the sample

corresponded to a person of masculine or feminine

gender, this, for its later use in algorithms of data

mining. In the same way, three columns were

excluded, because two of them were signal of the

gyroscope integrated to the EEG device, and another

one, sampled an extra value to the 14 necessary

electrodes, that very concurrently took a null value.

At this stage, the R table objects are analyzed

through the application of clustering, classification

and automatic learning methods, for determining if

these algorithms were able to recognize or predict the

gender of the volunteer.

All the data mining techniques used in this project

took as parameters the columns corresponding to the

maximum, mean, median and variance values that the

EEG channels of the EmotivEpoc diadem produced,

and the rows represent the samples of the volunteers

during the time of exposure to the images.

3.1 Data Mining Algorithms

In this section we present the main results obtained

using K-means, K-medoids, hierarchical clustering,

support vector machines, decision trees, Naive Bayes,

and Adaboost.

In the results presented in the form of tables,

contractions will be used for naming generated

datasets, e.g. v1, v2 and v3, refer to violent images

one, two and three, while nv1, nv2 and nv3, refers to

non-violent images one, two and three, likewise, for

sets with maximum (max), mean (mean), median

(median) and variance (var), resulting in naming the

datasets generated in this way, for example: v1max,

v3mean, nv2median, nv5var.

When executing the K-means method, with two

and three clusters, a very large cluster were formed,

and one or two with few objects in it, as can be seen

in Table 1.

Table 1: K-means with maximum values using k=2 & k=3.

Clusters size

Data

k=2 k=3

Cluster

v1max 1 49 1 5 44

v2max 5 45 44 2 4

v3max 5 45 3 2 45

v4max 8 42 9 39 2

v5max 5 45 39 8 3

nv1max 1 49 40 1 9

nv2max 8 42 8 41 1

nv3max 1 49 45 4 1

nv4max 46 4 40 6 4

nv5max 7 43 7 1 42

We increased the “k” number of clusters to 10,

due to the results observed with 2 and 3 clusters,

trying to divide the main cluster who appeared using

k=2 & k=3, hoping that the new clusters could contain

sub-groups from one gender, or other classifications,

unfortunately the clusters created did not include sub-

groups from just one gender, and at that moment, we

did not asked to the volunteers for their personal

information, such as age, occupation, or other

relevant data.

Obtained results with k from 2 to 10 were very

similar, small clusters and always one of a larger size,

as can be seen in Table 2.

Table 2: K-means with maximum values using k=10.

Data

Cluster and size

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

v1max 1 1 2 7 1 3 4 1 6 24

v2max 5 6 2 1 4 3 1 3 24 1

v3max 1 19 1 2 7 1 2 9 2 6

v4max 18 1 11 6 2 1 1 1 5 4

v5max 3 4 5 2 1 11 15 4 3 2

nv1max 2 8 2 3 4 1 1 25 1 3

nv2max 16 3 1 1 1 4 9 8 3 4

nv3max 1 1 1 1 13 26 1 1 1 4

nv4max 1 1 3 4 1 1 19 10 5 5

nv5max 1 2 28 1 2 1 1 9 4 1

When applying the K-medoids method, using any

of the two functions of R, pam () and pamk (), the

results obtained did not generate any distinction

between the genders. As discussed in Tables 3 and 4,

on the use of these methods, there was the same

problem as with the use of K-means, a cluster was

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formed that contained most of the objects, and

another one with little amount of data.

Table 3: pam() with mean values using k=2.

Data

Clusters Size

C1 C2

v1mean 43 7

v2mean 48 2

v3mean 45 5

v4mean 43 7

v5mean 49 1

nv1mean 49 1

nv2mean 48 2

nv3mean 49 1

nv4mean 47 3

nv5mean 49 1

Table 4: pam() with mean values.

Data

Clusters Size

C1 C2 C3 C4

v1mean 41 1 7 1

v2mean 48 2

v3mean 45 5

v4mean 43 7

v5mean 49 1

nv1mean 49 1

nv2mean 48 2

nv3mean 49 1

nv4mean 47 3

nv5mean 49 1

In the call to the hierarchical clustering method it

was noticed that was generated a similar result to the

previously described clustering methods, when

partitioning in 10 clusters, the dendrogram printed by

the method showed a cluster with most objects and

very small ones, It can also be seen in Figure 2 that

objects or samples belonging to both genders (M and

F) are housed in the formed clusters.

Figure 2: Hierarquical Clustering using k=10.

Training a SVM for classification and regression

involves solving a quadratic optimization problem.

Using a standard quadratic problem solver for

training an SVM would involve solving an

exponential problem. To handle this issue, methods

like SMO (Platt 1998), chunking (Osuna et al., 1997)

and simple SVM (Vishwanathan et al., 2003) exist

that iteratively compute the solution of the SVM and

scale O(N2.5). In package e1071 (Karatzoglou et al.,

2006), used in this project, the training patterns,

called support vectors, carry all relevant information

about classification problem.

The results obtained using the SVM algorithm on

the different tables didn’t show a good solution when

classifying test data. As can be seen in Tables 5 and

6, where the percentage of correctness and error

obtained on the classification of the selected samples.

Table 5: SVM with maximum values using lineal kernel.

Data

Error Success

v1max 42.1053 57.89474

v2max 38.4615 61.53846

v3max 71.4286 28.57143

v4max 50 50

v5max 42.8571 57.14286

nv1max 50 50

nv2max 38.4615 61.53846

nv3max 41.1765 58.82353

nv4max 57.8947 42.10526

nv5max 37.5 62.5

Table 6: SVM with variance values using lineal kernel.

Data

Error Success

v1var 28.5714 71.42857

v2var 66.6667 33.33333

v3var 41.1765 58.82353

v4var 36.3636 63.63636

v5var 50 50

nv1var 50 50

nv2var 44.4444 55.55556

nv3var 46.1538 53.84615

nv4var 50 50

nv5var 38.4615 61.53846

The results obtained using the Naive Bayes

algorithm yielded very different percentages of error

and correctness in classifying the test data, as can be

Analysis of Brain Waves in Violent Images - Are Differences in Gender?

411

seen in Tables 7 and 8, showing a very erratic

classification in the majority of the tests performed.

Table 7: Naïve Bayes with mean values.

Data

Erro

Success

v1mean 27.2727 72.72727

v2mean 41.6667 58.33333

v3mean 41.6667 58.33333

v4mean 42.8571 57.14286

v5mean 60 40

nv1mean 41.1765 58.82353

nv2mean 20 80

nv3mean 40 60

nv4mean 60 40

nv5mean 33.3333 66.66667

Table 8: Naïve Bayes with maximum values.

Data

Error Success

v1max 42.8571 57.14286

v2max 50 50

v3max 55.5556 44.44444

v4max 28.5714 71.42857

v5max 50 50

nv1max 63.6364 36.36364

nv2max 55.5556 44.44444

nv3max 36.3636 63.63636

nv4max 44.4444 55.55556

nv5max 45.4545 54.54545

The results obtained using the classification

algorithm with a decision tree, showed a very low

percentage of correctness in the gender classification

of the test objects as can be seen in Tables 9 and 10.

Table 9: Decision tree with maximum values.

Data

Erro

Success

v1max 53.8462 46.15385

v2max 61.1111 38.88889

v3max 52.9412 47.05882

v4max 53.3333 46.66667

v5max 58.8235 41.17647

nv1max 56.25 43.75

nv2max 50 50

nv3max 73.3333 26.66667

nv4max 57.1429 42.85714

nv5max 53.8462 46.15385

Table 10: Decision tree with mean values.

Data

Erro

Success

v1mean 68.75 31.25

v2mean 52.6316 47.36842

v3mean 57.8947 42.10526

v4mean 53.8462 46.15385

v5mean 53.8462 46.15385

nv1mean 52.9412 47.05882

nv2mean 70 30

nv3mean 60 40

nv4mean 62.5 37.5

nv5mean 61.5385 38.46154

The results obtained using Adaboost on the data

set did not obtain a good solution when classifying

samples by gender, obtaining very high percentages

of error, as shown in Tables 11 and 12.

Table 11: Adaboost with maximum values.

Data

Error Success

v1max 42.1053 57.89474

v2max 38.8889 61.11111

v3max 42.8571 57.14286

v4max 60 40

v5max 27.7778 72.22222

nv1max 52.9412 47.05882

nv2max 47.0588 52.94118

nv3max 30 70

nv4max 42.1053 57.89474

nv5max 50 50

Table 12: Adaboost with median values.

Data

Error Success

v1median 58.3333 41.66667

v2median 18.75 81.25

v3median 43.75 56.25

v4median 50 50

v5median 35.2941 64.70588

nv1median 41.6667 58.33333

nv2median 37.5 62.5

nv3median 66.6667 33.33333

nv4median 47.0588 52.94118

nv5median 30 70

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4 CONCLUSIONS

A database was obtained through the EmotivEpoc

EEG device and specialized software for the

collecting raw data which was analyzed with several

algorithms and data mining methods, in order to

determine if there was a difference in gender when

observing violent images. The database was built,

showing for thirty seconds, five violent images and

five non-violent images (three seconds per image) to

a group of 50 volunteers, of whom half were women

and the other half men.

The K-means method applied to all generated

tables, didn’t show good results, performing a

separation of 2, 3 and 10 clusters, in all cases, was

created a cluster which groups the majority of the

objects of both genders, and others clusters were very

small, grouping up to a single object, from which it’s

deduced that it’s not a good classification.

The Hierarquical method used didn’t obtain

different results to the K-means, the cut was carried

out to 10 clusters and it’s observed that the clusters

contain one or very few objects of both genders, and

there are one or two clusters of bigger size, that

contain the majority of samples for both genders. So

we didn’t get a good gender classification with this

Hierarquical method. Using the K-medoids method to

the calculated values tables, good results weren’t also

generated, using the pam() function of R, results

obtained were similar to K-means with two clusters,

since a cluster contained almost all of the samples,

and in these clusters, no gender classification was

found. With the pamk() function of R, we obtained

similar results to those of the pam() function,

however in some cases, it generated one or two more

clusters, although this didn’t result in a better

classification, since a large cluster was maintained,

there wasn’t any classification that could be identified

due to gender.

The Support Vector Machine (SVM) with linear

kernel, didn’t produce better results than the

clustering methods mentioned above, reaching, in the

worst cases, a success rate that was around 30-45%,

considering that it didn’t obtain a good classification

of gender due to it classified all the samples of test

like a single gender.

The Naive Bayes classification algorithm

presented results with very little success percentage

on the test data, the less successful tests were around

40% and the best classification was between 70% and

80%, although there were many tests with results

between these cases, it was not possible to obtain

good conclusions for using this algorithm in gender

classification of objects.

The decision trees methods gave us very high

rates of error in the gender classification, reaching

over 70% error and not less than 50% on the test data.

This indicates that this algorithm was not useful for

the classification of the samples of the database.

Finally, the Adaboost algorithm registered an

error rate between 40% and 70%, just as with the

percentage of success in the tests, the model created

fails to distinguish with certainty the samples in order

to classify them by gender.

Summarizing, the results obtained when using

clustering methods didn’t achieve a minimum

classification, as the number of clusters increased, the

cluster in which the majority of the samples were

concentrated didn’t decrease considerably its size,

and only generated other clusters with even a single

object. Thus, the results of these clustering methods

were not expected, since they failed to recognize or

classify the samples of different gender and the

generated clusters were very different in size and

samples of the same gender contained.

About using Support Vector Machines, Naïve

Bayes and Adaboost, the obtained results were not as

expected, since the formed models failed to perform

a classification of the test data with a considerable

percentage of success, reaching a success rate that

was around 40% to 60%.

Considering this, can be told that none of the

algorithms and methods presented and used here were

able to classify a reaction of viewing violent images

per gender.

In the execution of this project the specific

objectives were fulfilled, based on the results

obtained from the application of data mining

algorithms, it was not possible to determine if there is

a difference in gender when observing violent and

non-violent images. Probably the study should be

carried out with a larger number of people, belonging

to a more specific range of age, and a community

better delimited to avoid in a certain way that isolated

cases affect the methods of data mining, and so

perhaps to obtain other conclusions.

Other factors may affect outcomes, such as age,

environment, vision problems, past events that may

psychologically affect the human being, use of drugs,

medicines, or substances can affect the nervous

system

In the development of the project we presented

certain problems that were solved with a better use of

the R environment for data management, although the

notions acquired from the R language were sufficient

for the application of data mining is expected to

extend this knowledge of Data mining in the future.

In order to carry out related future work, the

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413

opinion of experts on violence should be considered

in order to determine the initial classification of the

images to be used, it should also be taken into account

that gender is not the only thing that can be inferred

in the reaction of the human brain before the

visualization of violent images or not.

Also, the creation of a more extensive database

with a greater number of participants, in order to be

able to contemplate cases that reacted abnormally to

the presence of violence could help in the training

stage for several algorithms such as SVM or

Adaboost. It could also be an option, to use videos

with violent or non-violent content instead of images

for future works.

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