Single Channel Eeg Device For The Analysis Of Person State Of Depression

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Single Channel EEG Device for The Analysis of Person State of Depression 1

P. Amsaleka 2Dr. S. Mythili 1 PG Scholar 2Associate Professor 1,2 Department of Electronics and Communication 1,2 PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.

Abstract The persons state (i.e., normal or depressed) are identified by monitoring the brain signal. Now, a days a normal person faces a lot of problems in their day to day life and this at one stage made them depressed. The human brain wave has alpha, beta, theta and delta frequencies. The changes in these frequencies is only responsible for changes in the person’s current state. When the alpha and delta wave is higher it indicates that the person is depressed. For analyzing these signals a device called Electroencephalogram (EEG} device is used. Depression is a common mental disorder characterized by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite feeling tiredness and poor concentration. Here the brain wave is picked from the scalp of the person who is rest state by using silver chloride (AgCl) surface electrode and the obtained signal is given to the developed EEG device containing differential amplifier (TL072 and TL074 ) and filter containing low pass and high pass filter. And the filtered output is seen using Digital Signal Oscilloscope (DSO). The normal and the depressed persons signal is seen in the DSO. Using the difference in their amplitude and frequency the state of the person is identified. Here the state of both normal and depressed person is analyzed. Further the processor is used for the analysis of depression or normal and it is displayed using LCD display.

Keyword- EEG, Depression, Electrode __________________________________________________________________________________________________

I. INTRODUCTION Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment (such as an EEG), measuring electricity levels over areas of the scalp. The combination of electrical activity of the brain is commonly called a brainwave pattern, because of its cyclic, "wave-like" nature.

The electrical activity in the brain will change depending on what the person is doing. For instance, the brainwaves of a sleeping person are vastly different than the brainwaves of someone wide awake. Over the years, more sensitive equipment has brought persons closer to figuring out exactly what brainwaves represent and with that, what they mean about a person's health and state of mind

II. RELATED WORK In UASN, a lot of researches have been proposed for data aggregation and data transmission based on clustering scheme to minimize energy consumption and extends the network lifetime. Ovaliadisk k et. Al. [5] proposed a new approach for better recovery of cluster head nodes in UWSN in which data dissemination protocol is responsible to distribute management command to all sensor nodes in the network and to update their configuration parameters.

The author goyal n et. al. [7] proposes to design a fuzzy based clustering and aggregation technique for UWSN. In this technique the parameter residual energy, distance to sink, load and link quality are considered as input to fuzzy logic. Based on the output of fuzzy logic module, appropriate cluster heads are selected and act as aggregator nodes.

Han g j et. al. [3] propose an attack resistant trust model which consists of three types of trust metrics which are link trust, data trust and node trust. During the process of trust calculation, unreliability of communication channel and mobility of underwater environment are carefully analyzed. It is used to address issue like Data forwarding, deployment and localization in UWSN under different condition.

Han et. Al. [6] presented a novel KNN (k-nearest neighbor algorithm) query algorithm based on grid division routing where the connectivity of adjacent grid centers forms the itinerary.

A. Types of Brain Wave The brain waves can be observed with an EEG (or an “Electroencephalograph�) a tool that allows researchers to note brain wave patterns. The five brain waves in order of highest frequency to lowest are as follows: gamma, beta, alpha, theta, and delta.

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Single Channel EEG Device for The Analysis of Person State of Depression (GRDJE / CONFERENCE / ICIET - 2016 / 037)

Beta waves are known as high frequency low amplitude brain waves that are commonly observed while a humans are awake. They are involved in conscious thought, logical thinking, and tend to have a stimulating affect.

Alpha wave frequency range bridges the gap between a person’s conscious thinking and subconscious mind. In other words, alpha is the frequency range between beta and theta. It helps to calm down when necessary and promotes feelings of deep relaxation. If a person become stressed, a phenomenon called “alpha blocking”.

Delta wave frequency range is involved in daydreaming and sleep. Theta waves are connected to experiencing and feeling deep and raw emotions. Too much theta activity may make people prone to bouts of depression and may make them “highly suggestible” based on the fact that they are in a deeply relaxed, semi-hypnotic state.

In Fig 1.1 the five brain waves in order of highest frequency to lowest are as follows: gamma, beta, alpha, theta, and delta .When slower brain waves are dominant a person can feel tired, slow, sluggish, or dreamy. The higher frequencies are dominant when a person feel wired, or hyper-alert. Brainwave speed is measured in Hertz (cycles per second) and they are dived into bands slow, moderate, and fast waves.

Fig. 1: brain wave

B. EEG Electrode The surface electrode is the one used in EEG must create an interface between an ionic solution (the subject) and a metallic conductor (the electrode). This leads to half- cell potential which can be quite large relative to the signal being recorded. To minimize the problem of polarization of the electrode, some small metal discs usually made of stainless steel, tin, gold or silver covered with silver chloride (AgCl) coating are used. Ag / AgCl electrodes have superior performance for EEG measurement and it is also recognized as low noise reference electrode. These sensors are less prone to artifacts than metal electrode. They on the scalp on special position. And these positions are specified by international 10/20 system. Each electrode is labelled with letter and number. The refers to area of the brain under laying the electrode and number denotes the right and left side of the head.

III. METHODOLOGY A single channel EEG device is designed using EEG electrode, amplifier and filter circuit Iis shown in Fig 2.1. The data is collected using the developed EEG device form 5 adults under the age group 24 - 29 years while they were in rest state. Then EEG signal is viewed using Digital Storage Oscilloscope. From the viewed signal measure the amplitude and frequency. By the measured frequency the person’s brain wave range is measured.

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Single Channel EEG Device for The Analysis of Person State of Depression (GRDJE / CONFERENCE / ICIET - 2016 / 037)

Fig. 2: block diagram

A. Electrode Placement Single channel containing 3 surface electrode is used. The three surface electrode are positive, negative and ground. The forehead of the subject is cleaned well and the electrode gel is applied on the forehead. The electrode gel is applied to minimize the impedance of EEG signal, so that the signal can be easily picked up by the electrode. After this, out the three electrode the positive and negative electrode is placed on the both sides of the eye and the ground electrode is placed on the earlobe. By these three electrode the EEG signal is picked and it is given to the developed EEG device containing amplifier and filter.

B. Amplifier and Filters The circuit diagram consists of 3 dual Differential Amplifier and a low pass and high pass filters is shown in Fig 2.2. The three differential amplifier are one TL072 amplifier of gain 10dB and two TL074 amplifier. Here the first TL074 has gain of 20dB and the second amplifier have gain of 100dB. The signal picked from the subject using 3 electrode. The signal picked up by the electrode needs to be amplified so it is amplified by the first dual differential Operational Amplifier(OP Amp) TL072 and then given to next TL074 dual Op Amp and filtered using low pass filter and then given to next TL074 dual Op Amp and then given to high pass filter. And the output is given to Digital Storage Oscilloscope for further analysis.

1) Differential OP AMP TL072 and TL074 The TL07XX JFET-input operational amplifier is designed to offer a wider section than any previously developed operational amplifier family . Each of these JEFT- input operational amplifier incorporates well matched high voltage JFET and bipolar transistors in a monolithic IC. The devices feature high slew rates, low input bias and offset currents and low offset – voltage temperature coefficient. The low offset voltage, temperature coefficient. The low harmonic distortion and low noise make TL072 and TL074 suited for pre-amplification purpose.

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Single Channel EEG Device for The Analysis of Person State of Depression (GRDJE / CONFERENCE / ICIET - 2016 / 037)

Fig. 3: circuit diagram

2) Low Pass and High Pass Filter It passes signals with a frequency lower than a certain cut off frequency and attenuates signals with frequencies higher than the cut off frequency. The amount of attenuation for each frequency depends on the filter design. The filter is sometimes called a high-cut filter, or treble cut filter in audio applications. A low-pass filter is the opposite of a high-pass filter Low-pass filters exist in many different forms, including electronic circuits (such as a hiss filter used in audio), anti-aliasing filters for conditioning signals prior to analog-to-digital conversion, digital filters for smoothing sets of data, acoustic barriers, blurring of images, and so on.

The amount of attenuation for each frequency depends on the filter design. A high-pass filter is usually modeled as a linear time-invariant system. It is sometimes called a low-cut filter or bass-cut filter. High-pass filters have many uses, such as blocking DC from circuitry sensitive to non-zero average voltages or radio frequency devices.

C. Digital Storage Oscilloscope Digital storage oscilloscope (DSO) is used for viewing the EEG signal from amplifier and filter circuit. The amplitude and the frequency of both normal and abnormal is note and the level of increase in the amplitude and frequency is observed.

IV. EXPERIMENTAL SETUP

Fig. 4: Single Channel EEG Device

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Single Channel EEG Device for The Analysis of Person State of Depression (GRDJE / CONFERENCE / ICIET - 2016 / 037)

A. SUBJECT PREPARATION FOR EEG Two young adults voluntarily participated in this study .The average age was 23 to 27 years and average weight from 50 -70Kg .

The experiment is carried out on both the subjects. The subject is placed in the comfortable chair and the electrodes are placed on the scalp of for certain time after cleaning the fore head with electrode gel and the positive and negative electrodes are placed on the forehead and the ground electrode is placed on the right leg. The test is takes place under rest state of the person .Then based on the frequency and amplitude variation in the subjects , the state of person is identified

Fig. 5: Subject Undergoing EEG Test B. Output Waveform of Subjects The output wave form of both normal and depressed subject in rest state with amplitude and frequency is observed from the fig 3.2 and fig 3.3.

SUBJECT SUBJECT 1 SUBJECT 2

AMPLITUDE 2 mv 2 mv

FREQUENCY 46.73 HZ 17.4 HZ

Fig. 6: Wave Form of Depressed Subject

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Single Channel EEG Device for The Analysis of Person State of Depression (GRDJE / CONFERENCE / ICIET - 2016 / 037)

Fig. 7: Wave Form of Normal Subject

V. RESULT The single channel EEG device was designed using differential Op Amp and filter. Here 3 electrode is used to collect the brain wave of the subjects. The frequencies of brain wave like alpha, beta, delta and theta changes is the reason for the changes in persons state. The frequency and amplitude is measured from the DSO and the increase in the frequency is noted. There is also some aspects in which the device can be enhanced in future. Here amplitude and frequency is viewed in DSO and increase in frequency is directly measured but in future it is statistically analyzed. In the future work processor is implemented and it is used to analyze the subjects current state (i.e., normal or depressed) and the mental status is displayed using LED or LCD .And the signal is now collected from 5 subjects but in future signal will be collected from 30 subjects .

REFERENCES [1] YashikaKatyal, Suhas V Alur, ShipraDwivede, Menaka R ,” EEG Signal and Video Analysis Based Depression Indication” IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) 2014.

[2] Subha D. Puthankattil, Paul K. Joseph, ” Analysis of EEG Signals Using Wavelet Entropy and Approximate Entropy: A Case Study on Depression patients” [3] International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering Vol:8, No:7, 2014.

[4] AshishPanat and Anita Patil,”Analysis of emotion disorders based on EEG signals of Human Brain”International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012.

[5] Subha D. Puthankattil and Paul K. Joseph,“Classification of eeg signals in normal and depression conditions by ann using rwe and signal entropy” Journal Of Mechanics In Medicine And Biology • October 2012.

[6] XiaoweiLia, Bin Hua,,TingtingXua, JiShena, MartynRatcliffeb,” A study on EEG-based brain electrical source of mild depressed subject” Elsevier Ireland Ltd 2015.

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