Page 57 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
P. 57
ognizing the subject exposure from the
EEG signals with artificial neural networks
Sašo Pavlič Sašo Karakatič
University of Maribor, Faculty University of Maribor, Faculty
of Electrical Engineering and of Electrical Engineering and
Computer Science Computer Science
Koroška cesta 46 Koroška cesta 46
Maribor, Slovenia Maribor, Slovenia
saso.pavlic@student.um.si saso.karakatic@um.si
ABSTRACT gather, they fail to reduce it. It is for this reason that the phenomena
limit their ability to direct concentration and learning. In this state,
The paper presents the analysis of Electroencephalography (EEG) we find the brain in a locked-in repetitive state, because in that state
brain waves from the Emotiv Insight device with machine learning, we dream or are drowsy.
more specifically neural networks. The captured EEG data
represents the input data into a machine learning model, which was Theta (3−8 Hz)
used to determine when and where the required patterns appear.
The experiment of the developed method of capturing data and Also classified as slower brain activity. The connection can be
model usage was carried out by exposing the test subject to the made with creativity, intuition, daydreaming and fantasy. It also
alternating selected images and capturing the EEG brain waves covers memories, emotions and feelings. Theta waves can be
with the Emotiv Insight device. The captured EEG data served as a expressed through prayer, meditation and spiritual capture. It can
dataset from which the artificial neural network classification be said to occur between waking consciousness and sleeping. When
model learnt to successfully recognize when a test subject was theta wave is optimal, it allows for flexible and complex behavior
exposed to one type of image and when to another. Convolutional structures such as learning and remembering. The imbalance of
and recurrent neural network models were constructed and tested these waves may indicate illness or stress present.
to evaluate the performance of recognition of subject exposal.
Alfa (8–12 Hz)
Keywords
Normal alpha status allows for fast and efficient task management.
Electroencephalography, Neural Networks, Machine Learning, In this condition, most people feel relaxed and calm. You could say
EEG signals that this wave is like a bridge between the conscious and the
unconscious. The alpha state is associated with extraversion,
1. INTRODUCTION creativity (when solving a problem or listening), and having mental
work. When the alpha waves are at the optimum range, we
Recently the analysis of Electroencephalography (EEG) data has experience well-being, see the world positively, and feel a sense of
gained much attention with the development of new measuring calm. This situation is one of the most important when learning and
techniques and the advancement of the machine learning using information already learned, such as work and education.
algorithms and methods. Simpraga et al. [1] proposed a machine
learning technique for detection of cholinergic and Alzheimer’s Beta (12–38 Hz)
disease. Boashash and Ouelha presented a method with machine
learning for detection of seizures of newborns [2]. Vanegas et al. The ripple is typical of "fast" activities. This wave is taken as a
presented a machine learning method for detecting of Parkinson’s normal rhythm and is the dominant wave when the person is
disease [3]. In the same manner, our research was focused on the collected or upset with the eyes open. Waves also occur in listening,
analysis of EEG data and recognition of subject exposures based on thinking, analytical problem solving, decision making, information
the EEG data with machine learning. Recognizing the simple processing, etc. Because of its relatively wide range, this wave is
subject visual exposures can be used in various fields, from user divided into low, medium and high beta waves.
experience, marketing and numerous psychology experiments [4],
but there is a lack of research demonstrating the usage of neural Gama (38–42 Hz)
networks for this case. This paper intends to fill in this gap.
It is a unique frequency wave that is present in all parts of our
1.1 Overview of EEG brains. When they have to process certain information from
different parts, it is precisely the 40 Hz frequency that combines the
There are four different EEG frequency bands. necessary brain regions for simultaneous processing of data. When
we remember something well, it's at 40 Hz activity.
Delta (0.5−3 Hz)
2. READING EEG AND ANALYSIS
The lowest frequency of brain waves moving below 3 Hz occurs
primarily in deep sleep. This frequency is prevalent in infants up to For recording the brainwaves we have been using BCI Emotiv
one year of age. It is also present between the 3rd and 4th stages of Insight, which has the excellent API for accessing that data directly
sleep. Delta waves are reduced in very intense concentration and from the device using Bluetooth protocol. With the API we
when we use our thinking processes very actively. Interest is found managed to get the raw EEG values for each sensor out of five. That
in individuals who have problems with comprehension and data was received in JSON format. Next, to the values from the
learning. They naturally magnify delta waves; when they want to device, we have been also adding the marker which was the
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.57-60 57
Koper, Slovenia, 10 October
EEG signals with artificial neural networks
Sašo Pavlič Sašo Karakatič
University of Maribor, Faculty University of Maribor, Faculty
of Electrical Engineering and of Electrical Engineering and
Computer Science Computer Science
Koroška cesta 46 Koroška cesta 46
Maribor, Slovenia Maribor, Slovenia
saso.pavlic@student.um.si saso.karakatic@um.si
ABSTRACT gather, they fail to reduce it. It is for this reason that the phenomena
limit their ability to direct concentration and learning. In this state,
The paper presents the analysis of Electroencephalography (EEG) we find the brain in a locked-in repetitive state, because in that state
brain waves from the Emotiv Insight device with machine learning, we dream or are drowsy.
more specifically neural networks. The captured EEG data
represents the input data into a machine learning model, which was Theta (3−8 Hz)
used to determine when and where the required patterns appear.
The experiment of the developed method of capturing data and Also classified as slower brain activity. The connection can be
model usage was carried out by exposing the test subject to the made with creativity, intuition, daydreaming and fantasy. It also
alternating selected images and capturing the EEG brain waves covers memories, emotions and feelings. Theta waves can be
with the Emotiv Insight device. The captured EEG data served as a expressed through prayer, meditation and spiritual capture. It can
dataset from which the artificial neural network classification be said to occur between waking consciousness and sleeping. When
model learnt to successfully recognize when a test subject was theta wave is optimal, it allows for flexible and complex behavior
exposed to one type of image and when to another. Convolutional structures such as learning and remembering. The imbalance of
and recurrent neural network models were constructed and tested these waves may indicate illness or stress present.
to evaluate the performance of recognition of subject exposal.
Alfa (8–12 Hz)
Keywords
Normal alpha status allows for fast and efficient task management.
Electroencephalography, Neural Networks, Machine Learning, In this condition, most people feel relaxed and calm. You could say
EEG signals that this wave is like a bridge between the conscious and the
unconscious. The alpha state is associated with extraversion,
1. INTRODUCTION creativity (when solving a problem or listening), and having mental
work. When the alpha waves are at the optimum range, we
Recently the analysis of Electroencephalography (EEG) data has experience well-being, see the world positively, and feel a sense of
gained much attention with the development of new measuring calm. This situation is one of the most important when learning and
techniques and the advancement of the machine learning using information already learned, such as work and education.
algorithms and methods. Simpraga et al. [1] proposed a machine
learning technique for detection of cholinergic and Alzheimer’s Beta (12–38 Hz)
disease. Boashash and Ouelha presented a method with machine
learning for detection of seizures of newborns [2]. Vanegas et al. The ripple is typical of "fast" activities. This wave is taken as a
presented a machine learning method for detecting of Parkinson’s normal rhythm and is the dominant wave when the person is
disease [3]. In the same manner, our research was focused on the collected or upset with the eyes open. Waves also occur in listening,
analysis of EEG data and recognition of subject exposures based on thinking, analytical problem solving, decision making, information
the EEG data with machine learning. Recognizing the simple processing, etc. Because of its relatively wide range, this wave is
subject visual exposures can be used in various fields, from user divided into low, medium and high beta waves.
experience, marketing and numerous psychology experiments [4],
but there is a lack of research demonstrating the usage of neural Gama (38–42 Hz)
networks for this case. This paper intends to fill in this gap.
It is a unique frequency wave that is present in all parts of our
1.1 Overview of EEG brains. When they have to process certain information from
different parts, it is precisely the 40 Hz frequency that combines the
There are four different EEG frequency bands. necessary brain regions for simultaneous processing of data. When
we remember something well, it's at 40 Hz activity.
Delta (0.5−3 Hz)
2. READING EEG AND ANALYSIS
The lowest frequency of brain waves moving below 3 Hz occurs
primarily in deep sleep. This frequency is prevalent in infants up to For recording the brainwaves we have been using BCI Emotiv
one year of age. It is also present between the 3rd and 4th stages of Insight, which has the excellent API for accessing that data directly
sleep. Delta waves are reduced in very intense concentration and from the device using Bluetooth protocol. With the API we
when we use our thinking processes very actively. Interest is found managed to get the raw EEG values for each sensor out of five. That
in individuals who have problems with comprehension and data was received in JSON format. Next, to the values from the
learning. They naturally magnify delta waves; when they want to device, we have been also adding the marker which was the
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference DOI: https://doi.org/10.26493/978-961-7055-82-5.57-60 57
Koper, Slovenia, 10 October
