Low Cost Electroencephalogram (EEG)

=Problem Definition=
In order to better educate high school students about brain function, we are
developing an educational, low cost EEG that can be made in classrooms
by students. During this year, we are going to determine the least
expensive and most easily reproducible way to create and EEG for under
$100.
=Background=
The biggest barrier to introducing high school students to basic
neuroscience is the staggering cost of an EEG and the corresponding
software. Instead of spending $800 on an EEG device like Emotive, and
hundreds of dollars per month on the related software, this new EEG
design will deliver open source software and instructions for an easily
reproducible hardware design to open neuroscience and basic engineering
to all high school students.
=Specifications=
 Project Specifications
Design Area 	Specification
Hardware 	
* The total hardware cost must be under $100
* The hardware must be able to be built by high school students
* There must be 16 channels
User Interface (UI)	
* The UI must be easy to use for students
* The UI should display the frequencies measured
* Since there should be no cost associated with the UI, we should use Python
=Design Considerations=
 Design Considerations
Design Area 	Consideration
PCB Hardware 	
* The PCB design must allow high school students to easily solder through-hole components. 
* The PCB layout must be small to reduce cost of manufacturing.
Microcontroller	
* The microcontroller must have 16 analog inputs to accommodate 16 EEG channels. 
* The on-board ADC must have a resolution that samples the EEG signals sufficiently. 
* In order to send the EEG signal to the computer as fast as possible, the microcontroller needs to have an RS232 to USB module.
User Interface 	
* The UI needs to be able to communicate to the microcontroller using serial communication
* Python includes the Pyserial package that makes serial communication very easy
=Project Learning=
=== Electrode Research ===
We have tested several different types of electrodes to use for the EEG. We bought a sample pack of medical grade gold cup electrodes in order to get a base line signal to compare our electrodes to. The main candidates that we have tested are silver cup electrodes, 3D printed electrodes, and DIY electrodes using metal plates. The table below shows the finding of our testing. From this testing we decided to use silver cup electrodes because they are less expensive than gold cup electrodes but measure the same quality of signal. 
 Electrode Research
Electrode Type	Pros	Cons
Gold Cup 	
* Medical Grade
* Durable
* Works well for low frequencies	
* Expensive
Silver Cup 	
* Medical Grade 
* Durable
* Less expensive than gold cup electrodes	
* May not last as long as gold cup electrodes
150px|center 	
* Free
* Easily modified
* Reproducible	
* Fragile
* Did not measure signal as well as silver or gold cup electrodes
=== Signal Research ===
The EEG signals are voltage fluctuations resulting from the currents produced by neurons in the brain. These EEG signals have very small magnitudes, typically around 50μV. The frequencies of the EEG signals that we are measuring range from 1Hz-50Hz. The following table describes the ranges of frequencies of EEG signals. 
 EEG Frequencies
Frequency Range 	Description
Delta (0Hz-3Hz) 	Delta waves to be the highest in amplitude and the slowest waves. They are normal as the dominant rhythm in infants up to one year and in stages 3 and 4 of sleep. It is usually most prominent frontally in adults.
Theta (3Hz-8Hz) 	Theta waves are classified as "slow" activity. They are perfectly normal in children up to 13 years and in sleep but abnormal in awake adults.
Alpha (8Hz - 13Hz)	Alpha waves appears when closing the eyes and relaxing, and disappears when opening the eyes or alerting by any mechanism (thinking, calculating). They are the major rhythms seen in normal relaxed adults. They are present during most of life especially after 13 years old.
Beta (13Hz - 30Hz)	Beta waves are the high frequency waves most commonly found in awake humans. They are channeled during conscious states such as cognitive reasoning, calculation, reading, speaking or thinking.
=== Hardware Research ===
==== Instrumentation Amplifiers ====
The instrumentation amplifier is a differential amplifier that amplifies the difference between a channel input and the reference signal. It also reduces common mode noise from the signals. 
There will be 1 instrumentation amplifier per channel.
none|left|400px|Instrumentation Amplifier 
==== Filters ====
The signals from the electrodes are very noisy. The biggest source of noise that we have found is 60Hz electrical hum from the power system. This frequency is very close to the frequencies that we are attempting to measure which makes it difficult to remove. This noise reduces measurement accuracy therefore, it needs to be removed. We are removing the noise by using a low pass filter with a cutoff frequency of 55Hz. In order to have a steeper roll-off, we are using a Chebyshev filter. 
none|left|400px|Chebyshev Filter 
=Hardware Design=
 Hardware Stages
Stage 	Description
Current Limiting Resistors	The electrodes are prone to a build up of a high DC voltage and ESD voltage that could potentially damage the instrumentation amplifier. The current limiting resistors are used to prevent large currents from entering the inputs of the instrumentation amplifier.
Instrumentation Amplifier 	The instrumentation amplifier (in-amp) is used to remove the common mode noise between the channel and the reference node. The amplifier also amplifies the differential signal by 12. The extra noise from contact with skin and other sources will be removed and the signal on the output will be the amplified EEG signal from the channel.
High Pass Filter 	Electrodes are susceptible to a phenomenon called "Electrode Polarization", where a large DC voltage is built up on the electrode. Therefore, a high pass filter with a very small cutoff frequency is implemented after the in-amp to remove the DC voltage before the signal is amplified. If the DC was amplified, it could potentially damage the circuit.
Non-Inverting Amplifier 	An amplifier with a gain of 50 is used to amplify the signal. After the amplifier, the signal will have an amplitude of ~50mV. This will allow the ADC to measure the changes in the signal with more precision.
Low Pass Filter 	In order to prevent aliasing when sampling the signal with the ADC, a low pass filter is used to remove any high frequencies above 100Hz. Aliasing would cause the signal to be distorted and would change the frequency components.
Buffer 	An ADC is a large capacitive load that could affect the performance of the opamps if connected directly. Therefore, a buffer is added between the output of the filters and the ADC.
none|left|1200px|LTspice Schematic 
The channel input signal used for the LTspice simulation is a 100µV, 50Hz sine wive added to a 10mV, 10kHz sine wave to represent the EEG signal and the common mode noise. 
none|left|1200px|Input Signal 
The resulting output signal is a 50Hz signal with an amplitude of ~60mV. This is what we would expect from a 100µV, 50Hz input signal.
none|left|1200px|Output Signal
=Team Members=
	
Name: Corey Knapp 
Major: Electrical Engineering 
Hometown: Clarkston, WA 
Responsibility: UI Design, Wikimaster 
Email: knap4020@vandals.uidaho.edu
	
Name: Allison Ellingson 
Major: Electrical Engineering 
Hometown: Boise, ID
Responsibility: Electrode Design, Scribe, Portfolio Master 
Email: elli7374@vandals.uidaho.edu
	
Name: Yiting Gao 
Major: Electrical Engineering
Hometown: Suzhou, China
Responsibility: Research, Finance 
Email: gao4247@vandals.uidaho.edu
	
Name: Ostin Arters 
Major: Biological Engineering
Hometown: Sun Valley, ID
Responsibility: Electrode Design, Headset Design, Research 
Email: arte8658@vandals.uidaho.edu
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=Additional Documentation=
Project Schedule
Schedule
Meeting Minutes
9/6 Meeting Minutes
9/13 Meeting Minutes
9/20 Meeting Minutes
9/25 Meeting Minutes
10/2 Meeting Minutes
10/16 Meeting Minutes
10/23 Meeting Minutes
10/30 Meeting Minutes
Presentations
Design Review
Client Interview
Client Interview