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1. Signal Processing and Machine Learning
Approaches for Electrooculography
Signals
by
Dr. Anirban Dasgupta, PhD
Assistant Professor
Dept. of Electronics and Electrical Engineering
IIT Guwahati
Guest Lecture
On
Organized by
IEEE SPS Student Branch, IIT Kharagpur

2. As eye moves from center toward
periphery,
● retina approaches one
electrode
● cornea approaches the other
Change in dipole orientation
causes a change in the electric
potential field
Electrooculography (EOG): The Concept
Signal is a measure of potential
difference between the cornea
and retina
EOG is an inexpensive method
for recording of eye movements
The eye can be modeled as a
dipole with its positive pole at
the cornea and its negative pole
at the retina
Resulting electrical signal is
called the electrooculogram
Potential arises due to the
hyperpolarization and
depolarization of the neurons in
the retina.

3. Electrooculography (EOG): Recording Protocol
Vertical Paradigm
Six pairs of ocular muscles
control eye movements
Three paradigms of recording the
EOG
Horizontal Paradigm Hybrid Paradigm
Electrodes are located near the
canthus
Capturing the horizontal eyeball
movements
Most common paradigm
Electrodes are located above
eyebrow and bottom of eye
Capturing the vertical eyeball
and eyelid movements
Useful in blink analysis
Five electrodes are used
Capturing the vertical and
horizontal movements
Cross channel information is
useful
Only three electrodes required Only three electrodes required
Both paradigms combined
Several paradigms involving
random electrode positions

4. Electrooculography (EOG): Applications
Estimating Eye Gaze Angle Detecting Directional Eye Movements
Rehabilitation
Engineering
Diagnosis of
Ocular Diseases
Cognitive
Research
Applications
Compute Ocular Parameters
Affective
Computing
EOG-controlled
wheelchair1
● Nystagmus
● Vitamin A
Deficiency
● Degenerative
Myopia
● Retinal
Disorders
● Oguchi Disease
User Activity
Recognition
● Copying a text
● Reading a printed
paper
● Taking handwritten
notes
● Watching a
● video
● Browsing the Web Cognitive fatigue in drivers
● Fear
● Stress
● Anxiety
● Anger
● Depression
● Surprise
1
Barea et al. , “Smart Wheelchairs and Brain-Computer Interfaces”, Mobile Assistive Technologies, 2018

5. Electrooculography (EOG): Signal Characteristics
Frequency range is 0.1 to 20 Hz
EOG amplitude lies between 100-3500 μV
up to 16 μV per degree of horizontal movement
up to 14 μV per degree of vertical movement
EOG signal quality is affected by:
● metabolic changes in the eye
● nature of the sensors

6. Electrooculography (EOG): Sensors
Active Passive
Components
Dry Wet
Conducting Medium
1
Pic courtesy: Active Electrodes - Open BCI
Have a
compensation
circuitry along
with sensors1
No compensation
circuitry, just the
sensors
Sensors connect
directly with skin
Need a conducting
medium2
2
Pic courtesy: Compumedics, USA

7. Electrooculography (EOG): Challenges
Artifacts arise from muscle
potentials and small
electromagnetic disturbances due
to cables or surrounding power
line interference
EOG analysis is difficult when
the subject executes any head or
body part movement which leads
to non-stationarity of the signal
Sensor noise yields poor
signal-to-noise ratio (SNR) which
varies with the sensor quality
Signatures of eye movements are
difficult to preserve while
denoising EOG

8. Eye Movement: The Types
Eye Movements
Eyeball Movement
Fixations
Micro-sacc
ades
Ocular
Drifts
Ocular
microtremors
Smooth
pursuits
Saccades
Vergence
movements
Convergence Divergence
Rolling
Vestibulo-
ocular
movements
Eyelid Movement
Blinks
Prolonged
Eyelid
Closure

9. Electrooculography (EOG): Pipeline
Sensor
Signal
Conditioning
Band-pass
filtering
Amplification
ADC
Sampling
Rate
Bit-size
Transmission
UART
USB
I2C
SPI
Active/
Passive
Dry/ Wet
Trend Estimation
Linear
Spline
Adaptive
Empirical Mode
Decomposition
High-pass
filtering
Band-pass
filtering
Median filtering
Wavelet filtering
Amplitude
Thresholding
Velocity
Thresholding
Wavelet
Thresholding
Recursive State
Estimation
Time-series
Motifs
Blink Rate
Saccadic Ratio
Saccadic
Duration
Saccadic Peak
Velocity
EOG Signal
Acquisition
Baseline
Wander
Removal
Denoising
Eye
Movement
Classification
Parameter
Estimation

10. Electrooculography (EOG): Baseline Wander Removal
One of the major non-stationarities of the signal
Effect where the signal level moves up and down
rather than being straight
What is baseline wander?
What causes baseline wander?
Improper electrodes
Interfering background signals
Electrode polarization
EOG Signal Model

11. Electrooculography (EOG): Baseline Wander Removal
Empirical Mode
Decomposition (EMD)
High Pass Filtering
Methods
Windowed mean removal
Linear spline fit
Cubic spline fit
Method Time (×10
-3
s) p-value of ADF test
(×10
-3
)
Windowed mean
removal
1.2 2.92
Linear spline fit 4.1 1.34
Cubic spline fit 5.3 0.97
FIR high pass filter 4.3 2.11
EMD 6.8 0.91

12. Electrooculography (EOG): Denoising
Existing Methods
Batch-processing
Band-pass Filtering
EMD
Wavelet-based Denoising
Median Filtering
Recursive
Kalman Filter
Particle Filter

13. Electrooculography (EOG): Denoising
Anirban Dasgupta, and Aurobinda Routray, “Piecewise empirical mode
Bayesian estimation-A new method to denoise electrooculograms.”, Biomedical
Signal Processing and Control, Elsevier, vol. 70, pp. 102-109, 2021.
Find breakdown
points
Divide the
signal into
sub-signals
Find intrinsic
modes of each
sub-signal
Remove lower
order intrinsic
modes as noise
Concatenate the
reconstructed
signal
Remove joining
artefacts
Noisy EOG signal
Denoised EOG signal

14. Electrooculography (EOG): Denoising
Method
CPU Time (×10
-3
s) GPU Time (×10
-3
s) SNR (×10
-3
) MSE (×10
-2
)
%
Preservation
of blinks
%
Preservation
of saccades
Band pass filter 0.8 0.036 27.8946 5.13 94.59 98.32
EMD 9.6 4.2 30.1247 6.17 93.88 98.47
Wavelet-denoising 11.3 0.512 29.1131 4.45 93.17 97.71
Median filter 0.4 0.019 25.8564 4.32 92.94 97.55
PEMBE
2.09×10
3 22.6 34.0661 9.98 94.12 99.08

15. Electrooculography (EOG): Classification
Classification Signal Processing
Amplitude and
velocity thresholding
Time-domain
matching
Wavelet-thresholding
Machine Learning
Shallow
Bayesian Learning
k-Nearest Neighbors
Decision Trees /
Random Forests
Deep
Convolutional
Neural Networks
Recurrent Neural
Networks
LSTM
GRU

16. Electrooculography (EOG): Movement Signatures

17. Electrooculography (EOG): Movement Signatures
Signal
processing
approaches
Machine
Learning
approaches

18. Conclusion
Discussed the nature, acquisition,
applications and research issues
in EOG signals
The main steps in EOG
processing include baseline
wander removal, denoising, and
movement classification
For denoising, signal processing
approaches are well-established
For classification, ML methods
are coming up which can
challenge signal processing
approaches

19. Electrooculography (EOG): The Concept
References
Anirban Dasgupta, and Aurobinda Routray, “Piecewise empirical mode Bayesian estimation-A new method to
denoise electrooculograms.”, Biomedical Signal Processing and Control, Elsevier, vol. 70, pp. 102-109, 2021.
Suvodip Chakraborty, Anirban Dasgupta, and Aurobinda Routray, “Localization of Eye Saccadic Signatures in
Electrooculograms using Sparse Representations with Data driven Dictionaries”, Pattern Recognition Letters,
Elsevier, vol. 139, pp. 104-111, 2020.
Anirban Dasgupta, Suvodip Chakraborty, and Aurobinda Routray, “A two-stage framework for denoising
electrooculography signals”, Biomedical Signal Processing and Control, Elsevier, vol. 31, pp. 231-237, 2017.
Anwesha Sengupta, Anirban Dasgupta, Aritra Chaudhuri, Anjith George, Aurobinda Routray, and Rajlakshmi Guha,
“A Multimodal System for Assessing Alertness Levels due to Cognitive Loading”, IEEE in Transactions on Neural
Systems and Rehabilitation Engineering, vol. 25, no. 7, pp. 1037 - 1046, 2017.
Desmond, Paula A., and Gerald Matthews. “Implications of task-induced fatigue effects for in-vehicle
countermeasures to driver fatigue.” Accident Analysis & Prevention 29.4 (1997): 515-523.
Kołodziej, Marcin, et al. "Fatigue Detection Caused by Office Work With the Use of EOG Signal." IEEE Sensors
Journal 20.24 (2020): 15213-15223.