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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, โ retina approaches one electrode โ 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 โ Nystagmus โ Vitamin A Deficiency โ Degenerative Myopia โ Retinal Disorders โ Oguchi Disease User Activity Recognition โ Copying a text โ Reading a printed paper โ Taking handwritten notes โ Watching a โ video โ Browsing the Web Cognitive fatigue in drivers โ Fear โ Stress โ Anxiety โ Anger โ Depression โ Surprise 1 Barea et al. , โSmart Wheelchairs and Brain-Computer Interfacesโ, Mobile Assistive Technologies, 2018
- 5. Electrooculography (EOG): Signal Characteristics Frequency range is 0.1 to 20 Hz EOG amplitude lies between 100-3500 ฮผV up to 16 ฮผV per degree of horizontal movement up to 14 ฮผV per degree of vertical movement EOG signal quality is affected by: โ metabolic changes in the eye โ 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 (ร10 -3 s) p-value of ADF test (ร10 -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, โPiecewise empirical mode Bayesian estimation-A new method to denoise electrooculograms.โ, 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 (ร10 -3 s) GPU Time (ร10 -3 s) SNR (ร10 -3 ) MSE (ร10 -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 2.09ร10 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, โPiecewise empirical mode Bayesian estimation-A new method to denoise electrooculograms.โ, Biomedical Signal Processing and Control, Elsevier, vol. 70, pp. 102-109, 2021. Suvodip Chakraborty, Anirban Dasgupta, and Aurobinda Routray, โLocalization of Eye Saccadic Signatures in Electrooculograms using Sparse Representations with Data driven Dictionariesโ, Pattern Recognition Letters, Elsevier, vol. 139, pp. 104-111, 2020. Anirban Dasgupta, Suvodip Chakraborty, and Aurobinda Routray, โA two-stage framework for denoising electrooculography signalsโ, 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, โA Multimodal System for Assessing Alertness Levels due to Cognitive Loadingโ, IEEE in Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 7, pp. 1037 - 1046, 2017. Desmond, Paula A., and Gerald Matthews. โImplications of task-induced fatigue effects for in-vehicle countermeasures to driver fatigue.โ Accident Analysis & Prevention 29.4 (1997): 515-523. Koลodziej, Marcin, et al. "Fatigue Detection Caused by Office Work With the Use of EOG Signal." IEEE Sensors Journal 20.24 (2020): 15213-15223.