This document provides an overview of Suraj Bhamra's master's thesis project on applying open source EEG to control pulse width modulation waves. It includes biographical information on Bhamra, an agenda for the presentation, background on EEG and brain waves, a discussion of the theory and modeling behind a MEMS EEG sensor design, and plans for the system architecture and application of using brain activity to control a prosthetic hand. Finite element analysis was performed to analyze the von Mises stress on the MEMS sensor under compression and shear forces to ensure it would not tear or deform under pressure.

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Application of Open Source EEG
to Control PWM Wave
Advisor: Coach Zohdy
Student: Suraj Bhamra

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Before We Begin…
Engineering Student Medical Student
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Personal Bio
 Born in Windsor, Canada (don’t
take it against me!)
 Owner of Negative and Print
Print Shop in Detroit, MI
 Hobbies include lifting weights,
working on cars and
motorcycles
 Two Truths, One Lie
 I’ve driven an Abrams Tank
 I’ve never been to Africa
 I once was pulled over doing
160+ mph
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4. +
Professional Bio
 Oakland University BSEE 2011
 Met Coach in 2009!
 Oakland University MSEE 2013
 Oakland University PhD 201X
 US Army TACOM Intern (2009-
2011)
 US Army TACOM Electrical
Engineer (2011-Present)
 Network integration
 Radio communications
 Prototype Engine Testing
Academic Professional
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Presentation Agenda
 Problem Statement
 EEG Background
 Approach
 Theory
 Anatomy
 Types of Brain Waves
 Fourier Transform
 MEMS Mode Frequencies
 Application
 MEMS Sensor Design
 Implementation
 Headset
 Data traffic
 Present Issues
 Future Work
 Conclusions
 Demo
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Problem Statement
Why EEG? Why the brain?
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Problem Statement (cont’d)
Very Little is Still Understood about the Brain
 BRAIN (Brain Research through Advanced Innovative
Neurotechnologies) Initiative introduced by White House in April 2013
 100 billion neurons and 100 trillion connections in the brain
 Find underlying causes (and cures) for Alzheimer’s disease, Parkinson’s
disease, autism, epilepsy, schizophrenia, depression, etc
Open Source is the Way of the Future!
 More people experimenting with EEG = more awareness = more
discovery
 Current EEG technology is very expensive and heavily restricted
 Use commercially available technology to better understand EEG
and it’s scientific benefits
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Problem Statement (cont’d)
 Electrical impulses generated by nerve firings in the brain can
be measured by electrodes placed on the scalp
 The EEG gives us a coarse view of neural activity
 EEG activity is very small signal with amplitudes in the μV
(microvolts)
 Main frequencies of interest range from 4Hz to 30Hz
 Electrical activity picked up by electrodes, fed through signal
processor and displayed
How do EEGs work?
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Problem Statement
How do EEGs work?
Electrode placement is key
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Problem Statement (cont’d)
 Use commercial off the shelf (COTS) items to create an open
source EEG
 Record brain activity using this open source EEG
 Use brain activity to trigger a PWM wave
 Use this PWM wave to actuate a servo
 Use the servo to actuate a prosthetic hand (flexion and
extension)
My Solution to Current Challenges
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Theory
The foundation of research.
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Anatomical Introduction
Brain Activity
 EEG (Encephalography) data
 Brain wave classification
 Parsing and grouping of desired
and undesired signals (signal to
noise ratio)
Physical Interface
 Physical makeup of skin
 Layers of skin and their growth
characteristics
 Electrode-skin interface
 Renewal of skin and
interference of signal chain
Med School Members Don’t Criticize Me Too Much!
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Anatomical Introduction
 Three primary layers of skin
 Epidermis (outermost) layer
has three sub layers (stratum
germinativum, stratum
granulosum, and stratum
corneum)
 Cells originate in deepest
layer of epidermis, the
stratum germiativum
Human Skin Composition
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Anatomical Introduction
 New skin cells move outward
to the stratum corneum
 As they move outward they
lose their nuclear material
 Below the epidermis is the
Dermis and Subcutaneous
layer
Human Skin Composition cont’d
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Anatomical Introduction
Lobes of the Brain
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Types of Brain Waves
Alpha
 Rhythmic in nature
 Frequencies ranges from
~8Hz to ~13Hz
 Amplitude ranges from 20μV
to 200μV
 Occur in an awake but
relaxed state
 Prominent in the occipital
and parietal lobes of the
brain
 Sensitive to external stimuli
 Usually measured with eyes
closed
 Visual stimuli causes
replacement with
asynchronous waves of
higher frequencies but lower
amplitudes
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Types of Brain Waves
Beta
 Amplitude ranges from 5μV to 10μV
 Generally associated with intense
mental activity
 Relaxed but alert
 More intense mental tasks (reading,
math, etc)
 Problem solving, mild anxiety
 High anxiety, OCD
 Attentive to external stimuli (such
as focus)
 Strong presence in frontal lobe
 Several types of beta brain waves
 Beta I (13Hz to 15Hz)
 Beta II (16Hz to 18Hz)
 Beta III (19Hz to 26Hz)
 Beta IV (27Hz to 32Hz)
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Types of Brain Waves
Theta and Delta
 Frequencies range from 4Hz
to 7Hz
 Occur in parietal and
temporal lobe
 Prominent in transition to
sleep from alpha relaxation
 Also found when waking up
from deep sleep
 Known as “stage 1 sleep”
 Frequencies smaller than
3.5Hz
 “Slowest” waves with
highest amplitude
 Commonly found during
sleep
 Emitted from cortex
 High delta may be result of
head injuries, strokes,
tumors, etc
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Traditional Instrumentation of Brain
Non-Invasive Electrodes
 Easier to instrument patient
 More susceptible to motion
artifacts
 Sensitive to about 10mV
 Arranged in network on patients
scalp
 Time consuming to set up
electrode array
Invasive electrodes
 More difficult to instrument
patient
 Less susceptible to motion
artifacts
 Sensitive to about 100μV
 “Needle” inserted directly into
scalp
 Requires surgery to install and
remove
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Importance of Frequency Domain
 The main advantage of frequency domain analysis is being
able to visualize the periodicities of the signal.
 This helps understand the physical nature of the signals.
 The signals used in this project are time based and being
able to visualize the periods of the functions helps in plotting
the waves in intuitive patterns
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Fourier Transform in EEG
The Fourier transform describes a signal x(t) as a linear superposition of sines
and cosines characterized by their frequency f.
Where,
This is also known as the Continuous Fourier Transform (CFT) of the signal x(t)
and can be understood as the inner product of the signal x(t) with a parent
function.
(1)
(2)
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Fourier Transform in EEG (cont’d)
This can now be written as
Where the inverse transform is given by (1). Since the mother
functions are orthogonal the Fourier Transform is unique.
For example we can consider an EEG signal consisting of N
discrete values sampled at a time Δt
Where xj is a measurement taken at time tj=to+jΔ
(3)
(4)
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Fourier Transform in EEG (cont’d)
The Discrete Fourier Transform (DFT) of this signal is now defined as,
Where k = 0,…,N-1
Its inverse transform can now be written as,
(5)
(6)
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Fourier Transform in EEG (cont’d)
Since we are considering real signals only, the following relationship is true
Therefore the discrete frequencies examined are given by (8) while the
resolution is given by (9)
Since the Fourier Transform gives N/2 independent complex coefficiants,
there are N values as in the original signal (nonredundant)
(7)
(8) (9)
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What Does This Mean?
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System Architecture Block Diagram
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MEMS EEG Sensor (New)
 Flexible substrate can conform
to patients body contours
 Easily removable
 Will not cause skin ulceration
 Silver layer makes it
transparent for X-rays
 Hydrogels use as conductive
medium
 Easy install, high resolution
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Natural Frequency and Mode Shape
 Insertion column displaced by shear force
 Releasing shear force causes beam to vibrate and displace from it’s
equilibrium state
 This results in a non-harmonic motion
 Certain initial displacements cause harmonic vibrations
 These initial deflections are mode shapes
 Corresponding frequencies are natural frequencies
 Dependent on material (Young’s Modulus, density) and dimensions of
cantilever beam
 MEMS be happy!
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Clamped-Free Cantilever Beam
Equation of Newtonian motion is given by
¶2
u
dt2
+ k
¶4
u
dx4
= f (x,t) = 0 (10)
¶2
u
dt2
+
EI
rA
¶4
u
dx4
= f (x,t) = 0 (11)
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Beam Boundary Conditions
Describe the initial boundary conditions.
u(0,t) = 0
ux (0,t) = 0
utt (0,t) = 0
uxxxx (0,t) = 0
u(0,t) = 0
uxx (0,t) = 0
(12)
(13)
(14)
Clamped
Free
Simply Supported
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Equation Setup
u(x,t) = F(x)G(t)
(15)
(16)
From the boundary conditions on the previous page we get the following equations
Plugging in (16) into the motion equation ( 10 and 11)
(17)
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Equation Setup Cont’d
Simplification of (8) results in
(18)
Combining like variables yields
(19)
Where –Β4 is a constant
b4
=
rAw2
EI
(20)
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Equation Setup Cont’d
Breaking up equation (10) yields two differential equations
¢¢¢¢F + b4
F = 0
(21)
(22)
Equation (21) has the general solution
G(t) = Acosb2
ct + Bsinb2
ct
While equation (22) has the general solution
F(x) = Acosbx + Bsinbx +Ccoshbx + Dsinhbx
(23)
(24)
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Equation Setup Cont’d
By our boundary conditions we see that
u(0,t) = ux (0,t) = 0
uxx (x,t) = uxxxx (x,t) = 0
(25)
Now using (25) and applying our boundary conditions we see
A + C = 0
B + D = 0
-Bsinbx - Acosbx + Dsinhbx + C coshbx = 0
-Bcosbx + Asinbx + Dcoshbx + Csinhbx = 0
(26)
Equation (26) can now be represented in matrix form
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Equation Setup Cont’d
0 1 0 1
1 0 1 0
-sinbx -cosbx sinhbx coshbx
-cosbx sinbx coshbx sinhbx
é
ë
ê
ê
ê
ê
ù
û
ú
ú
ú
ú
B
A
D
C
é
ë
ê
ê
ê
ê
ù
û
ú
ú
ú
ú
= 0 (27)
-sinbx - sinhbx -cosbx - coshb
-cosbx - coshb sinbx - sinhb
é
ë
ê
ê
ù
û
ú
ú
(28)
Solving the determinate yields the following frequency equation and frequencies
cosbnx =
1
coshbnx
wn =
bnEI
rA
(29) (30)
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Equation Setup Cont’d
The mode shapes can now be given by
Fn = An cosbnx + Bn sinbnx +Cn coshbnx + Dn sinhbnx (31)
Using the boundary condition information that C = -A and D = -B
-Bsinbx - Acosbx - Bsinhbx - Acoshbx
-B(sinbx + sinhbx) = A(cosbx + coshbx)
(32)
B = -A
cosbx + coshbx
sinbx + sinhbx
æ
èç
ö
ø÷
It is now obvious that
(33)
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Equation Setup Cont’d
The final mode shape equation can now be written as
Fn = -An -
cosbnl + coshbnl
sinbnl + sinhbnl
æ
èç
ö
ø÷ sinbnx + cosbnx +
cosbnl + coshbnl
sinbnl + sinhbnl
æ
èç
ö
ø÷ sinhbnx - coshbnx
é
ë
ê
ù
û
ú (34)
The first 6 mode shapes were calculated to see the natural harmonics and resonant
frequencies in the platinum covered silicon insertion column!
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Mode Shapes and
Natural Frequencies
Mode Shape Frequencies In HZ
Mode Shape 1: 3470884.361
Mode Shape 2: 21751660.994
Mode Shape 3: 60905280.627
Mode Shape 4: 119350041.902
Mode Shape 5: 197294170.950
Mode Shape 6: 294723328.582
Mode shapes determine silicon insertion columns will
not tear off Polyimide substrate!

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Application
Applying Theory
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System Architecture Block Diagram
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Initial MEMS EEG Sensor Design
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Initial MEMS EEG Sensor Design
 Polyimide substrate for flexibility
 Single crystal silicon columns coated with platinum for good
conductivity
 Crystalline silicon has predictable etch and growth properties
and is dimensionally stable to 1400 Celsius
 Silicon has favorable Youngs Modulus for strength
 Conductive material to transmit current (many options
explored)
 Gold strands placed in spikes to collect surface currents
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Model Geometry
 500μm x 500μm x 12.7μm
substrate layer
 Thickness of substrate
determined on commercially
available polyimide from
DuPont
 12.7μm provided best
combination of flexibility and
strength
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Model Geometry Cont’d
 4x4 array of insertion columns
inserted onto substrate
 Pierce epidermis to pick up
neural activity
 Radius of 10.3μm
 Height of 200μm
 Height subject to change due to
z-axis compression
(installation)
 Gold pads linked with gold fiber
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Boundary Condition Selection
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Model Mesh
 Free tetrahedral, brick, prism,
and pyramid-type mesh
explored
 Free tetrahedral is most
commonly used
 Smaller mesh yields more
accurate results but require
more computing resources
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Von Mises Shear Stress
Analysis
 Shear stress tested (translational)
 Ensure sensor does not tear or deform under pressure
 1e-2 N/m2 stress applied perpendicular to column
 Damped using Rayleigh damping (linear combination of mass
and stiffness)
 3% critical damping
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Von Mises Shear Stress
Analysis
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Von Mises Shear Stress
Analysis
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Compression Von Mises Stress
 Electrode pressed into
epidermis layer of skin
 Force is in the z-axis
 This force results in the
compression of insertion
columns
 Necessary to compute to test
compressional stability of
Silicon
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Compression Von Mises Stress
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Implementation
Putting Application into Action
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System Architecture Block Diagram
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Electrode and Headset
 Currently using NeuroSky ™ (TM) dry TGAM (ThinkGear ASIC
Module) based sensor
 Prorotype headset uses Gen I TGAM1 technology
 MindWave headset uses Gen II TGAM2 technology
 TGAT chip used in both headsets
 NeuroSky ™ (TM) eSense
 A/D
 Biomedical instrumentation amplifier
 Internal noise filter
 After understanding NeuroSky ™ (TM) data structure and
protocol we can design our own MEMS sensor
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Headset Function
 NeuroSky ™ (TM) chip trasmits information as raw bytes
 Arduino microcontroller parses information from raw byte stream into ASCII
string CSV
 NeuroSky ™ (TM) bytes are in proprietary format and cannot be changed
(hence motivation for designing our own sensor)
 Serial monitor breaks down data into the following format
 Signal strength (unsigned one byte integer from 0 to 200)
 NeuroSky ™ (TM) “eSense”
 Attention and Relaxation from 1 to 100 (40-60 neutral)
 RAW Wave Values (16 Bit)
 Signed 16 bit integer ranging from -32768 to 32767
 Two bytes (first high order bit of the twos compliment value, second is low order bit)
 Relative EEG strengths (Delta, Theta, low-Alpha, high-Alpha, low-Beta, high-Beta, low-
Gamma, mid-Gamma.
Data Breakdown
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System Architecture Block Diagram
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Headset Function
Real Serial Information
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System Architecture Block Diagram
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Headset Function
 Open source plotting tool
Processing used to visualize
data
 Data is read through the COM
port (indexed through the
Processing sketch) and mapped
to a real time plotter
 Datalogging functionality to be
implemented in a further
software release
Graphing Software
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System Architecture Block Diagram
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Headset Function
 Script BrainPWM running on
Arduino
 Responsible for mapping
NeuroSky ™ (TM) “eSense” to
a PWM value at a certain
threshold
 Pin 13 used on Arduino at 9600
baud rate
 Initialized state of pin is LOW
 State transitions to HIGH when
eSense reaches indexed valie
PWM Control
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Present Issues
What’s keeping us busy now?
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Present Issues
 Headset is highly susceptible to motion artifacts
 Connection strength is heavily dependant on external
conditions (humidity, temperature, conditions of skin)
 60Hz frequency filtering (Notch) not adequate and headset
experiences noise in certain environments with overhead
power lines
 No robust electrical isolation
Headset Reliability
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Present Issues
 NeuroSky ™ (TM) “black-box” values for attention and
relaxation
 We do not know the algorithms used for generation of these values
 Further simulation of MEMS sensor and studying gen I prototype will
allow us to come up with our own data scheme
 Reverse engineer NeuroSky ™ using MEMS technology
Data Values and Parsing
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Future Work
Our Next Step
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Future Work
 Concentrate on one specific part of architecture to study for
research
 Many options (MEMS sensor, filtering schemes, instrumentation
amplifiers, data structures, etc)
 Thus narrowing down area of interest
 Phase out prototype headset use and use research grade
NeuroSky ™ (TM) developer headset
 Further simulation of MEMS sensor
 Fabrication
 EMI/EMC
 Material science
 Continue working with Med School
Area of Interest
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Future Work
Hardware/Software Focus Areas
• Developer headset
• Wireless transmission (XBee, Bluetooth, etc)
• OpenBCI headset/microcontroller
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Conclusions
Current Outcomes
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Conclusions
 Proof of concept demonstrated in operational environment
 Data structure parsing and coding complete
 Real time data visualizer adapted successfully
 Headset integration complete
 Prototype headset and servo to be delivered to OU Med School
for further testing (March/April 2016)
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Conclusions
Linked to NeuroSky TM eSense (Concentration)
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References
 Arduino Brain Library Errors
http://forum.arduino.cc/index.php?topic=90605.0
 ECE690 BioFeedback (co-Author Abdrahamane Traore)
 NeuroSky ™ EEG Brainwave Data
https://github.com/kitschpatrol/Brain
 Programmer Error Arduino
http://forum.arduino.cc/index.php?topic=28686.0
 MindFlex EEG Setup
http://service.mattel.com/instruction_sheets/P2639-0920G1.pdf
 Quantitative Electroencephalography
http://www.sciencedirect.com/science/article/pii/S0987705308000233
 Quantitative analysis of EEG signals Time frequency methods and Chaos theory
https://vis.caltech.edu/~rodri/papers/thesis.pdf
 EEG With Arduino
https://sites.google.com/site/chipstein/home-page/eeg-with-an-arduino
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References (cont’d)
 Time Frequency Analysis of EEG Waveforms
http://www.timely-cost.eu/sites/default/files/ppts/2ndTrSc/Niko%20Busch%20-
%20Time%20frequency%20analysis%20of%20EEG%20data.pdf
 FFT of EEG Data
http://jama.jamanetwork.com/article.aspx?articleid=391249
 Time-Frequency Decomposition
http://sccn.ucsd.edu/eeglab/workshop06/handout TP_Jung_Workshop_Time_Frequency_Analysis.pdf
 Cassification of Electroencephalogram (EEG) Signal Based on Fourier Transform and Neural Network
http://ethesis.nitrkl.ac.in/4749/1/109EE0640.pdf
 Frequency Analysis of Healthy & Epileptic Seizure in EEG using Fast Fourier Transform
http://ijergs.org/files/documents/FREQUENCY-82.pdf
 Using Novel MEMS EEG Sensors in Detecting Drowsiness Application
https://www.cs.tau.ac.il/~nin/Courses/Seminar14a/Drowsiness.pdf
 ECE790 (co-author Darena and Mark)
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Backup Slides
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Arduino Code
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Function Overview
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