In the second experiment, a factorial design was employed with one factor being the dosage of JA, and the second being the dosage of ACC. If JA and ACC did not interact to affect a variable, then only the main effects of JA and ACC were considered. If JA and ACC interacted to affect a variable, then the nature of the interaction was determined, and the data were summarized in two-way tables.The Internet of Things is increasingly used by normal people. There will be 50 billion Internet of Thing devices by the 2030. More and more people have started to adopt and use the Internet of Thing devices in everyday life. This thesis aims to explore and study the possibility of implementing and using electroencephalography as controller in the Internet of Thing environment. Also, this thesis intends to study and integrate the human emotion with the Internet of Things Framework . This chapter introduces what Brain Computer Interface is and discusses the components of the Brain Computer Interface. In addition, this chapter explores some of the techniques used to measure the brain activity. Finally, this chapter discusses this research questions of this thesis.Brain Computer Interface is a communication method that depends on the neural activity generated by the brain regardless of peripheral nerves and muscles. BCI aims to provide a new channel of output for the brain controlled and adapted by the user. There are many Brain Computer Interface applications that can be implemented, such as applications for disabled people to interact with computational devices, blueberry container applications for gamers to play games with their thoughts, social applications to capture feelings and emotions, and application for human brain activities.
It is a medical imaging technique used to capture high quality pictures of the anatomy and physiological processes of the body. It uses powerful magnet, radio waves, and field gradients to generate images of the body. It is non-invasive, painless and does not use radiation . It can provide very detailed high resolution images of a body parts. In particular, it can capture very detailed high resolution images for the brain compared to other imaging techniques such as CT and X-ray because of its ability to differentiate between soft tissues of the body. However, due to the magnet effects, metallic items are not allowed during the scan which because they limit its applications.It is a special MRI technology that measures brain activity by detecting changes associated with blood flow. This technique relies on coupled cerebral blood flow and neuronal activation. The blood flow increases in a region when this region is in use. The idea of this technique lies in the amount of oxygenated and deoxygenated hemoglobin changes in the blood flow during the neural activity. The most common one is Blood Oxygenation Level Dependent fMRI which measures the ratio of Oxy-Hb to Deoxy-Hb in order to measure the oxygen consumption of active neurons. It is also invasive and has an excellent spatial resolution compared to EEG, and records signals from all the brain regions. However, this technique has the same limitations of the MRI technique.It is the technique used to measure the magnetic field over the head generated by the electric current in the brain. The most commonly used technology of MEG currently is SQUIDs. This technique allows capturing MEG of the head efficiently and rapidly. Also, this technique is non-invasive and can be used as complement for other techniques such as EEG and fMRI. Due to the fact that MEG uses magnetic fields,this technique makes less distortion than the electric fields. However, the same restriction applied on fMRI and MRI can be applied to MEG due the to its sensitivity for ferromagnetic.
An electroencephalogram is a method monitoring the electrical activity of the brain using small flat metal discs placed on the scalp. EEG measures voltage fluctuations resulting from brain cells communications via electrical impulse. In Particular when neurons are activated, ions such as Na+ , K+ and CI– are pushed through the neuron membrane. EEG is a weak signal and needs to be amplified in order to be displayed or stored on a computer. Two approaches to recording the EEG signal are invasive and non-invasive. In the invasive approach, the electrode is implanted inside the human brain, which requires surgery. In a non-invasive approach, electrodes are placed on the surface of the skull, which have many benefits such as risk free, easy setting, and repeating measurement. In addition, it is more favorable in developing and designing application for normal people. The focus of this thesis will be based on this non-invasive EEG technique .The first question of this thesis is how to integrate the low-quality cheap EEG headset, which has only one electrode located in forehead, with an Internet of Things framework. In order to do this we have to first build and design the EEG Server which is able to translate EEG signals into commands. Then, we must build an algorithm that construct different patterns from these commands, and these patterns will be used to control different Internet of Things devices. The expected outcome after integration and build, the different EEG pattern is the ability to control Internet of Thing devices such as Light turning on/off, music playing and etc.The second question of this thesis is how to build the EEG Edge which is able to classify between eye close and eye open states. In order to answer this question we will use an extension of the Internet of things framework that supports intelligent edge, which is presented in [38].
So, in order to build the EEG Edge we need to extract EEG features for different subjects and build the model that is able to classify between open and close eye states. There are different types of features that could be extracted from EEG raw signal. However, for this application we need only to extract the power spectrum density features. Lastly, we need to define the feature extraction extension which will contain the EEG features and define the execution extension which will contain the classifier model. The expected outcome after the integration will be the ability to classify eye states on the edge.The third question of this thesis is how to build a model that is able to detect and classify positive and negative emotions. In order to classify the emotions, different factors must be considered, which include participants, stimuli, the temporal window, and EEG features. Different EEG features will be extracted from EEG raw signal and these features include time domain features, frequency domain features, and nonlinear features. Different video clips will be used as stimuli in order to trigger different emotions. The expected outcome will be the ability to classify two different types of emotions, positive and negative emotions.EEG is the electrical activity measurement in the brain. The first measure for EEG was recored by Has Berger in 1924 using galvanometer. Based on the internal brain behavior or external stimulus, EEG varies in amplitude and frequency of the wave. The system contains a EEG headset, and this thesis used ”NeuroSky Mindwave Mobile” which is using Bluetooth connection to transfer the EEG signal. The EEG receiver records and receives the EEG signal coming from a EEG headset which is written in Python. I used the Wukong framework to deploy WuClass for the EEG, and WuClass for a controller on Intel Edison and Raspberry Pi. Figure shows the system architecture will be used in this thesis.There are a lot of commercial EEG headsets from the simplest ones to the more sophisticated one. Table compares different EEG headset. These different EEG headsets are able to capture different mental states, and different facial expressions. Both emotiv headsets, the EPOC+ and INSIGHT, capture excitement, frustration, engagement, meditation and affinity. Also, Emotiv headsets capture EEG bands which are Delta, Theta, Alpha, Beta, and Gamma. In addition, Emotiv headsets capture some facial expressions such as blinking, smiling, clenching teeth, growing blueberries in containers laughing and smirking. On the other hand, the Neurosky Mindwave Mobile is limited to capturing only two mental states which are meditation and relaxation. Finally, the Muse headset can capture positive and negative emotions. Also, the Muse headset captures EEG bands which are Delta, Theta, Alpha, Beta, and Gamma. In addition, the Muse headset also captures some facial expressions such as jaw clenching andeye blinking. Emotive EPOC+ sensors use saline soaked felt pad technology, and emotive INSIGHT sensors use long-life semi-dry polymer technology. Neurosky Mindwave Mobile and Muse sensors use long life dry technology.NeuroSky Mindwave Mobile consists of eight parts which are ear clip, ear arm, battery area, power switch, adjustable head band, sensor tip, sensor arm, and think gear chip. The operation of this device is based on two sensors to detect and filter EEG signals. The sensor tip on the forehead detects the electrical signal from the frontal lobe of the brain. The second sensor is an ear clip which is used as ground to filter out the electrical noise. Figure shows NeuroSky Mindwave Mobile and Figure shows the electrode position of NeuroSky Mindwave Mobile.
This thesis uses NeuroSky Mindwave Mobile for many reasons. First, this project aims to offer a low-cost system, which can be used by everyone. Second, NeuroSky Mindwave Mobile is highly resistant to noise and its signal is digitized before it is transmitted throughBluetooth. Third, NeuroSky Mindwave Mobile offers unencrypted EEG signal compared to the Emotive and Muse which are encrypted.It is NeuroSky algorithm to characterize mental states. This algorithm applied on the remaining signal that is acquired from removing the noise and the muscle movements of the raw brain wave signals. Two eSense signal are produced as a result of this algorithm: attention and meditation signals. These signals detect the concentration and relaxation of subject. The values of these signals range from 0-100 in which zero indicates low in concentration or in low in relaxation, and 100 indicates high in concentration or high in relaxation.One major limitation is the accuracy of the EEG signal captured by NeuroSky Mindwave Mobile, because the NeuroSky Mindwave Mobile Mobile has only one electrode which is FP1. The problem with FP1 is its susceptibility to a lot of noise coming from eye movement and muscle movement. Another possible issue is comfort. One subject claims it is uncomfortable to wear. This is most likely due to the rigid headband design as well as the need for the ear clip as the reference sensor.In order to detect eye blinking, OpenCV library is used which is an open-source library of programming functions aimed to offer real-time computer vision. This library is used to detect eye blinking to trigger the system, hold on to a certain state, and change between different states. The algorithm used to classify blinking is Haar Cascades classifier which is a machine learning approach in which the cascade function is trained by a lot of negative and positive pictures, then used to detect objects in other image. In order to obtain EEG signal from Neurosky Mindwave Mobile I used an open-source API written in Python suggested by the Neurosky company. Two major libraries used are bluetooth headset.py and parser.py. The bluetooth headset library contains methods to connect the Mindwave Mobile to the computer via Bluetooth either by specifying a MAC address or by automatically searching for a device named ”Mindwave Mobile”. If it does not find a device automatically or with the MAC address specified, it will raise an error. The other library parser.py is specific to NeuroSky Mindwave Mobile device. There are two major classes in this library: ThinkGearParser and Time Series Recorder. It must first create a new Time Series Recorder object and then include this object into a Think Gear Parser object, which will package the EEG information and be able to display the data on the computer. The other important library that is necessary for WuKong integration is a socket which creates a new socket that points to the exact IP address of the peripheral device . In order to control a device Wuclass has to be designed. For this project, EEG Server is Wuclass that is able to receive the EEG signal and transform to different actions. Triggering an action will take around 10 seconds as shown in Figure . Figure explains and shows the flow of data and control of the system in Flow based program .