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DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
Mental Emotional Sentiment Classification with an EEG-
based Brain-Machine Interface
Jordan J. Bird
School of Engineering and Applied Science
Aston University
Birmingham, UK
birdj1@aston.ac.uk
Christopher D. Buckingham
School of Engineering and Applied Science
Aston University
Birmingham, UK
c.d.buckingham@aston.ac.uk
Anikó Ekárt
School of Engineering and Applied Science
Aston University
Birmingham, UK
a.ekart@aston.ac.uk
Diego R. Faria
School of Engineering and Applied Science
Aston University
Birmingham, UK
d.faria@aston.ac.uk
ABSTRACT
This paper explores single and ensemble methods to classify
emotional experiences based on EEG brainwave data. A
commercial MUSE EEG headband is used with a resolution of
four (TP9, AF7, AF8, TP10) electrodes. Positive and negative
emotional states are invoked using film clips with an obvious
valence, and neutral resting data is also recorded with no stimuli
involved, all for one minute per session. Statistical extraction of
the alpha, beta, theta, delta and gamma brainwaves is performed
to generate a large dataset that is then reduced to smaller datasets
by feature selection using scores from OneR, Bayes Network,
Information Gain, and Symmetrical Uncertainty. Of the set of
2548 features, a subset of 63 selected by their Information Gain
values were found to be best when used with ensemble classifiers
such as Random Forest. They attained an overall accuracy of
around 97.89%, outperforming the current state of the art by 2.99
percentage points. The best single classifier was a deep neural
network with an accuracy of 94.89%.
Keywords
Emotion Classification, Brain-Machine Interface, Machine
Learning.
1. INTRODUCTION
The proceedings are the records of the IAPE’18 conference. We
ask that authors follow some simple guidelines. In essence, we ask
you to make your paper look exactly like this document. The
easiest way to do this is simply to replace the content with your
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Autonomous non-invasive detection of emotional states is
potentially useful in multiple domains such as human robot
interaction and mental healthcare. It can provide an extra
dimension of interaction between user and device, as well as
enabling tangible information to be derived that does not depend
on verbal communication [1]. With the increasing availability of
low-cost electroencephalography (EEG) devices, brainwave data
is becoming affordable for the consumer industry as well as for
research, introducing the need for autonomous classification
without the requirement of an expert on hand.
Due to the complexity, randomness, and non-stationary aspects of
brainwave data, classification is very difficult with a raw EEG
stream. For this reason, stationary techniques such as time
windowing must be introduced alongside feature extraction of the
data within a window. There are many statistics that can be
derived from such EEG windows, each of which has varying
classification efficacy depending on the goal. Feature selection
must be performed to identify useful statistics and reduce the
complexity of the model generation process, saving both time and
computational resources during the training and classification
processes.
The main contributions of this work are as follows:
• Exploration of single and ensemble methods for the
classification of emotions.
• A high performing data mining strategy reaching
97.89% accuracy.
• The inclusion of facial EMG signals as part of the
classification process.
• A resolution of three emotional classes (positive,
neutral, negative) to allow for real world on mental
states that are not defined by prominent emotions.
• One Rule classification demonstrating how accurately
the AF7 electrode’s mean value classifies mental states.
The remainder of this paper will explore related state-of-the-art
research and provide the main inspiration and influences for the
study. It will explain the methodology of data collection, feature
generation, feature selection and prediction methods. The results
will be presented and discussed alongside comparable work,
followed by conclusions and future work.
DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
Figure 1. Di agr am to show Lövheim’ s Cube of
Emotional Categorization
2. RELATED WORK
Statistics derived from a time-windowing technique with feature
selection have been found to be effective for classifying mental
states such as relaxed, neutral, and concentrating [2]. An ensemble
method of Random Forest had an observed classification accuracy
of 87% when performed with a dataset which was pre-processed
with the OneR classifier as a feature selector. These promising
results suggested a study on classification of emotional states
using a similar exploration method would be similarly successful.
The best current state-of-the-art solution for classification of
emotional EEG data from a low-resolution, low-cost EEG setup
used Fisher’s Discriminant Analysis to produce an accuracy of
95% [3]. The study tried to prevent participants from becoming
tense and discourage blinking but the previous study [2] found
that EMG data from these activities helped classification because
blink rates are a factor in concentration for example. Hence the
new study described in this paper will explore classification of
emotions in EEG data when unconscious movements are neither
encouraged nor discouraged. Conscious extraneous movements
such as taking a sip of water will not be allowed because they just
form outlying or masking points in the data. For example, if the
people experiencing positive emotions are also drinking water, the
model will simply classify the electrical data that has been
generated by those movements. Stimuli to evoke emotions for
EEG-based studies are often found to be best with music [4] and
film [5]. This paper thus focuses on film clips that have audio
tracks (speech and/or music) to evoke emotions, similarly to a
related study that used music videos [6].
Common Spatial Patterns have proved extremely effective for
emotion classification, attaining an overall best solution at 93.5%
[7]. A MUSE EEG headband was successfully used to classify
high resolutions of valence through differing levels of enjoyment
during a certain task [8]. Deep Belief Network (DBN), Artificial
Neural Network (ANN), and Support Vector Machine (SVM)
methods have all been able to classify emotions from EEG data
was also found to be very effective with when considering binary
classes of positive and negative [9]. This study will build on all
these results using similar methods as well as an ensemble, to
exploit their differing strengths and weaknesses. The study also
supports the usage of a neutral class, for transition into real-world
use, to provide a platform for emotional classification where
emotions are not prominent. It adds valence or perceived
sentiment because this was previously found to be helpful in the
learning processes for a web-based chatbot [10].
3. BACKGROUND
3.1 Electroencephalography
Electroencephalography is the process using applied electrodes to
derive electrophysiological data and signals produced by the brain
[11] [12]. Electrodes can be subdural [13] ie. under the skull,
placed on and within the brain itself. Noninvasive techniques
require either wet or dry electrodes to be placed around the
cranium [14]. Raw electrical data is measured in Microvolts (uV)
at observed time t producing wave patterns from t to t+n.
3.2 Human Emotion
Human emotions are varied and complex but can be generalized
into positive and negative categories [15]. Some emotions overlap
such as ’hope’ and ’anguish’, which are considered positive and
negative respectively but that are often experienced
Table 1. Table to show Lövheim categories and their
encapsulated emotions with a valence label
Emotion
Category
Emotion/Valence
A
Shame (Negative) Humiliation (Negative)
B
Contempt (Negative) Disgust (Negative)
C
Fear (Negative)
Terror (Negative)
D
Enjoyment (Positive) Joy (Positive)
E
Distress (Negative) Anguish (Negative)
F
Surprise (Negative) (Lack of Dopamine)
G
Anger (Negative) Rage (Negative)
H
Interest (Positive) Excitement (Positive)
contemporaneously: e.g. the clearly doomed hope and
accompanying anguish for a character’s survival in a film. This
study will concentrate on those emotions that do not overlap, to
help correctly classify what is and is not a positive experience.
Lövheim’s three-dimensional emotional model maps brain
chemical composition to generalised states of positive and
negative valence [16]. This is shown in Fig. 1 with emotion
categories A-H from each of the model’s vertices, further detailed
in Table I. Various chemical compositions can be mapped to
emotions with positive and negative classes. Furthermore, studies
show that chemical composition influences nervous oscillation
and thus the generation of electrical brainwaves [17]. Since
emotions are encoded within chemical composition that directly
influence electrical brain activity, this study proposes that they
can be classed using statistical features of the produced
brainwaves.
DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
Figure 2. A simpl ified di agram of a ful ly-connected feed
forward deep neural network.
3.3 Machine Learning Algorithms
The study in this paper applies a number of machine learning
algorithms. One Rule (OneR) classification is a simplistic
probabilistic process of selecting one attribute from the dataset
and generating logical rules based upon it. For example:
"IF temperature LESS THAN 5.56 THEN December"
and
"IF temperature MORE THAN 23.43 THEN July"
are rules generated based on a temperature attribute to predict the
month (class). This model will identify the strongest attribute
within the dataset for classifying emotions
Decision Trees follow a linear process of conditional control
statements based on attributes, through a tree-like structure where
each node is a rule based decision that will further lead to other
nodes. Finally, an end node is reached, and a class is given to the
data object. The level of randomness or entropy on all end nodes
is used to measure the classification ability of the tree. The
calculation of entropy is given as:
!
"
#
$
%&'
(
)*&+&,-./")*$&
0
*12 3 "4$
Entropic models are compared by their difference in entropy
which is information gain. A positive value would be a better
model, whereas a negative value shows information loss versus
the comparative model. This is given as:
5678
"
9:;
$
% &!
"
9
$
'!
"
9:;
$
: "<$
where E is the entropy calculated by Equation 1.
Support Vector Machines (SVM) classify data points by
generating and optimising a hyperplane to separate them and
classifying based on their position in comparison to the
hyperplane [18]. A model is considered optimised when the
average margins between points and the separator is at its
maximum value. Sequential Minimal Optimisation (SMO) is a
high-performing algorithm to generate and implement an SVM
classifier [19]. The large optimisation problem is broken down
into smaller subproblems, that can then be solved linearly.
Bayes’ Theorem [20] uses conditional probabilities to determine
the likelihood of Class A based on Evidence, B, as follows:
)
"
6=>
$
%)"?=@$A"@$
)">$ 3 "B$
For this study, evidence consists of attribute values (EEG
time-window statistics) and ground-truth training for determining
their most likely classes. A simpler version is known as
Naive Bayes, which assumes independence of attribute values
whether or not they are really unrelated. Classification of
Naive Bayes is adapted from Equation 3 as follows:
C
D
% E F
"
4:G:E
$
&H
"
IJ
$K
H
"
L*
=
IJ
$
M
*12 : "N$
where y is the class and k is the data object (row) that is being
classified.
Logistic Regression is a symmetric statistical model used for
mapping a numerical value to a probability, ie. hours of study to
predict a student’s exam grade [21]. For a binary classification
problem with i attributes, and β model parameters, the log odds l
is given as
, %&OPQ&
R
O*QL*
S
*1P
and thus the corresponding
odds of outcome are therefore given as
- %&TUVW&
R
UXWSX
Y
XZV
which
can be used to predict a model outcome based on previous data.
A Multilayer Perceptron is a type of Artificial Neural Network
(ANN) that predicts a class by taking input parameters and
computing them through a series of hidden layers to one or more
nodes on the final output layer. More than one hidden layer forms
a deep neural network and output layers can be different classes
or, if there is just one, a regression output. A simplified diagram
of a fully connected feed forward deep neural network can be seen
in Fig. 2. Learning is performed for a defined time and follows the
process of backpropagation [22], which is the process of deriving
a gradient that is further used to calculate weights for each node
(neuron) in the network. Training is based on reducing the error
rate given by the error function ie. the performance of a network
in terms of correct and incorrect classifications or total Euclidean
distance from the real numerical values. An error is calculated at
output and fed backwards from outputs to inputs.
3.4 Model Ensemble Methods
An ensemble combines two or more prediction models into a
single process. A method of fusion takes place to increase the
success rate of a prediction process by treating the models as a
sum of their parts.
Voting is a simple ensemble process of combining models and
allowing them to vote through a democratic or elitist process.
Each of the models are trained, and then for prediction, they
award vote v to class(es) via a specified method:
• Average of probabilities; v = confidence
• Majority vote; v = 1
• Min/Max probability v = average confidence of all
models
Following the selected process, a democracy will produce an
outcome prediction as that of the class that has received the
strongest vote or set of votes.
DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
Figure 3. EEG sensors TP9, AF7, AF8 and TP10 of the
Muse headband on the international standard EEG
placement system [26]
Random Forest forms a voting ensemble from Decision Trees
[23]. Multiple trees are generated on randomly generated subsets
of the input data (Bootstrap Aggregation) and then those trees, the
random forest, will all vote on their predicted outcome and a
prediction is derived. Adaptive Boosting is the process of creating
multiple unique instances of one type of model prediction to
effectively improve the model in situations where selected
parameters may prove ineffective [24]. Classification predictions
are combined and weighted after a process of using a random data
subset to improve on a previous iteration of a model. Combination
is given as:
[\
"
L
$
%&
(
]^"L$
\
^12 :"_$
where F is the set of t models and x is the data object with an
unknown class [25].
4. METHOD
The study employs four dry extra-cranial electrodes via a
commercially available MUSE EEG headband. Microvoltage
measurements are recorded from the TP9, AF7, AF8, and TP10
electrodes, as seen in figure 3. Sixty seconds of data were
recorded from two subjects (1 male, 1 female, aged 20-22) for
each of the 6 film clips found in Table II producing 12 minutes
(720 seconds) of brain activity data (6 minutes for each emotional
state). Six minutes of neutral brainwave data were also collected
resulting in a grand total of 36 minutes of EEG data recorded from
subjects. With a variable frequency resampled to 150Hz, this
resulted in a dataset of 324,000 data points collected from the
waves produced by the brain. Activities were exclusively stimuli
that would evoke emotional responses from the set of emotions
found in Table I and were considered by their valence labels of
positive and negative rather than the emotions themselves. Neutral
data were also collected, without stimuli and before any of the
emotions data (to avoid contamination by the latter), for a third
class that would be the resting emotional state of the subject.
Three minutes of data were collected per day to reduce the
interference of a resting emotional state.
Table 2. Source of Film Clips used as Stimuli for EEG
Brainwave Data Collection
Stimulus
Valence
Studio
Year
Marley and Me
Neg
Twentieth Century
Fox, etc.
2008
Up
Neg
Walt Disney Pictures,
etc.
2009
My Girl
Neg
Imagine
Entertainment, etc.
1991
La La Land
Pos
Summit
Entertainment, etc.
2016
Slow Life
Pos
BioQuest Studios
2014
Funny Dogs
Pos
MashupZone
2015
Table 3. Attribute Evaluation Methods used to Generate
Datasets for Model Training
Evaluator
Ranker Cutoff
No. Attributes
OneR
0.4
52
BayesNet
0.4
67
InfoGain
0.75
63
Symmetrical
Uncertainty
0.4
72
Participants were asked to watch the film without making any
conscious movements (eg. drinking coffee) to prevent the
influence of Electromyographic (EMG) signals on the data due to
their prominence over brainwaves in terms of signal strength. A
previous study that suggested blinking patterns are useful for
classifying mental states [2] d blinking patterns are useful for
classifying mental states [2] inspired this study to neither
encourage nor discourage unconscious movements. Observations
of the experiment showed a participant smile for a short few
seconds during the ‘funny dogs’ compilation clip, as well as
become visibly upset during the ‘Marley and Me’ film clip (death
scene). These facial expressions will influence the recorded data
but are factored into the classification model because they
accurately reflect behaviour in the real world, where these
emotional responses would also occur. Hence, to accurately model
realistic situations, both EEG and facial EMG signals are
considered as informative. To generate a dataset of statistical
features, an effective methodology from a previous study [2] was
used to extract 2400 features through a sliding window of 1
second beginning at t=0 and t=0.5. Downsampling was set to the
minimum observed frequency of 150Hz.
Feature selection algorithms were run to generate a reduced
dataset from the 2,549 source attributes. Chosen methods ranked
attributes based on their effectiveness when used in classification,
and a manual cutoff point was tuned where the score began to
drop off, therefore retaining only the strongest attributes. Details
of attribute numbers generated by each method can be seen in
Table III. The reduced dimensionality makes the classification the
classification experiments more tractable and within the remit of
given computational resources.
DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
5. PRELIMINARY RESULTS
Model training for each method was performed on every dataset
generated by the four methods shown in Table III. The
parameters, where required, were set to the following:
• 10-fold cross validation for training models (average of
10 models on 10 folds of data).
• A manually tuned deep neural network of two layers, 30
and 20 neurons on each layer respectively. Backward
propagation of errors. 500 epoch training time.
• All random numbers generated by the Java Virtual
Machine with a seed of 0.
• Ensemble voting based on Average Probabilities.
After downsampling, there were slightly more datapoints for the
neutral state, and thus to benchmark a Zero Rules (’most common
class’) classifier would classify all points as neutral. This was
33.58% and therefore any result above this shows useful rule
generation.
Models for ensemble were selected manually based on best
performance. Voting was performed on average probabilities
using the Random Tree, SMO, BayesNet, Logistic Regression,
and MLP models. Random Forests, due to their impressive
classification ability was attempted to be optimized by the
AdaBoost Algorithm.
Results of both single and ensemble classifiers can be seen in
Table IV. The best model, a Random Forest with the Infogain
dataset, achieved a high accuracy of 97.89%. The small amount
of classification errors came from a short few seconds of the half
an hour dataset, meaning that errors could be almost completely
mitigated when classifying in real time due to the sliding window
technique used for small timeframes t-n. Adaptive boosting was
promising for all Random Forest models but could not achieve a
score higher, pointing towards the possibility of outlying points.
For single classification, the multilayer perceptron was the most
consistently best model, showing the effectiveness of neural
networks for this particular problem.
The effectiveness of OneR classification showed that a certain
best attribute (mean value of AF7) existed that alone had a
classification ability of 85.27%. The rule is specified in Fig. 4.
The normalised mean value of the time windows extracted from
the AF7 electrode when observed show that minimum and
maximum values most commonly map to negative emotions,
whereas positive and neutral are very closely related, having rules
overlapping one another. One Rule classification improved over
the Zero Rule benchmark by over 50 points, and therefore would
have been an effective attribute to consider over others when it
came to utilising more than one of the attributes in the other
methods.
The two best models in our study are compared to the state of the
art alternatives in Table V. The method of generating attributes,
attribute selection via info gain and finally classification with a
Table 4. Classification Accuracy of Single and Ensemble Methods on the Four Generated Datasets
Dataset
Single Model Accuracy
Ensemble Model Accuracy
OneR
RT
SMO
NB
BN
LR
MLP
RF
Vote
AB(RF)
OneR
85.18
91.18
89.49
66.56
91.18
91.84
92.07
95.26
92.68
95.59
BayesNet
85.27
93.05
89.49
60.69
91.23
91.93
93.81
97.14
93.39
97.23
InfoGain
85.27
94.18
89.82
60.98
91.46
92.35
94.89
97.89
94.04
97.84
Symmetrical
Uncertainty
85.27
94.15
89.54
69.66
92.03
91.93
94.18
97.56
94.32
97.65
Table 5. An Indirect Comparison of this Study to Similar
Works Performed on Different Datasets
Study
Method
Accuracy
This study
InfoGain, RandomForest
97.89
Bos, et al. [3]
Fisher’s Discriminant
94.9
This study
InfoGain, MLP
94.89
Li, et al. [7]
Common Spatial Patterns
93.5
Li, et al.
Linear SVM
93
Zheng, et al. [9]
Deep Belief Network
87.62
Koelstra, et al. [6]
Common Spatial Patterns
58.8
Normalised mean value of the AF7 electrode:
< -460.0 -> NEGATIVE
< -436.5 -> POSITIVE
< -101.5 -> NEGATIVE
< 25.45 -> POSITIVE
< 25.85 -> NEUTRAL
< 26.25 -> POSITIVE
< 37.7 -> NEUTRAL
< 39.05 -> POSITIVE
< 43.599999999999994 -> NEUTRAL
< 63.95 -> POSITIVE
< 97.7 -> NEUTRAL
< 423.0 -> POSITIVE
>= 423.0 -> NEGATIVE
Figure 4. The most effective single rule for
classification.
DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
Random Forest outperforms an FDA model by 2.99 points.
Further work should be carried out to identify whether this
improved result was due to the methods chosen or the attribute
generation and selection, or possibly both.
6. DISCUSSION
The high performance of simple multilayer perceptrons suggests
neural network models can be effective, especially more complex
ones such as Convolutional Neural Networks (CNNs) that have
performed well in various classification experiments [27].
Similarly, ensemble and and Bayesian models are promising
avenues that could perform better with more advanced models,
such as Dynamic Bayesian Mixture Models (DBMM) [28] that
have previously been applied to statistical data extracted from
EEG brainwave signals.
Being able to recognise emotions autonomously would be
valuable for mental-health decision support systems such as
GRiST which is a risk and safety management system used by
mental-health practitioners and by people for assessing
themselves [29], [30]. Evaluations of emotions independent of
self-reporting would help calibrate the advice as well as guiding
more sensitive interactions. The measurement of brainwaves used
in this paper is too intrusive but would be useful for providing a
benchmark for finding other more appropriate methods.
7. CONCLUSION
This paper explored the application of single and ensemble
methods of classification to take windowed data from four points
on the scalp and quantify that data into an emotional
representation of what the participant was feeling at that time. The
methods showed that using a low resolution, commercially
available EEG headband can be effective for classifying a
participant’s emotional state. There is considerable potential for
producing classification algorithms that have practical value for
real-world decision support systems. Responding to emotional
states can improve interaction and, for mental-health systems,
contribute to the overall assessment of issues and how to resolve
them.
ACKNOWLEDGEMENT
This work was partially supported by the European Commission
through the H2020 project EXCELL (https://www.excell-
project.eu/), grant number 691829 (A. Ekart) and by the EIT
Health GRaCEAGE grant number 18429 awarded to C. D.
Buckingham.
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DISP '19, Oxford, United Kingdom
ISBN: 978-1-912532-09-4
DOI: http://dx.doi.org/10.17501........................................
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