Using natural head movements to continually calibrate EOG signals

Electrooculography (EOG) is the measurement of eye movements using surface electrodes adhered around the eye. EOG systems can be designed to have an unobtrusive form-factor that is ideal for eye tracking in free-living over long durations, but the relationship between voltage and gaze direction requires frequent re-calibration as the skin-electrode impedance and retinal adaptation vary over time. Here we propose a method for automatically calibrating the EOG-gaze relationship by fusing EOG signals with gyroscopic measurements of head movement whenever the vestibulo-ocular reflex (VOR) is active. The fusion is executed as recursive inference on a hidden Markov model that accounts for all rotational degrees-of-freedom and uncertainties simultaneously. This enables continual calibration using natural eye and head movements while minimizing the impact of sensor noise. No external devices like monitors or cameras are needed. On average, our method’s gaze estimates deviate by 3.54° from those of an industry-standard desktop video-based eye tracker. Such discrepancy is on par with the latest mobile video eye trackers. Future work is focused on automatically detecting moments of VOR in free-living

A MATLAB-BASED GUI FOR REMOTE ELECTROOCULOGRAPHY VISUAL EXAMINATION

In this work, a MATLAB-based graphical user interface is proposed for the visual examination of several eye movements. The proposed solution is algorithm-based, which localizes the area of the eye movement, removes artifacts, and calculates the view trajectory in terms of direction and orb deviation. To compute the algorithm, a five-electrode configuration is needed. The goodness of the proposed MATLAB-based graphical user interface has been validated, at the Clinic of Child Neurology of University Hospital of Ostrava, through the EEG Wave Program, which was considered as “gold standard” test. The proposed solution can help physicians on studying cerebral diseases, or to be used for the development of human-machine interfaces useful for the improvement of the digital era that surrounds us today

An Analysis of Eye Movements With Helmet Mounted Displays

Helmet or Head-Mounted Displays (HMD) applications have expanded to include a range from advanced military cockpits to consumer glasses. However, users have documented loss of legibility while undergoing vibration. Recent research indicates that undesirable eye movement is related to the vibration frequency a user experiences. In vibrating environments, two competing eye reflexes likely contribute to eye movements. The Vestibulo-ocular Reflex responds to motion sensed in the otoliths while the pursuit reflex is driven by the visual system to maintain the desired image on the fovea. This study attempts to isolate undesirable eye motions that occur while using a HMD by participants completing simple visual tasks while experiencing vertical vibration at frequencies between 0 and 10 Hz. Data collected on participants\u27 head and helmet movements, vibration frequency, acceleration level, and visual task are compared to eye movements to develop a method to understand the source of the unintended eye movements. Through the use of Electro- Oculography (EOG) eye movements were largest when a 4 Hz vibration frequency was applied, and are significantly different from the EOG signal at 2, 8 and 10 Hz. Stepwise regression indicated that head pitch acceleration and helmet slippage pitch acceleration were correlated with EOG values

Improving Engagement Assessment by Model Individualization and Deep Learning

This dissertation studies methods that improve engagement assessment for pilots. The major work addresses two challenging problems involved in the assessment: individual variation among pilots and the lack of labeled data for training assessment models. Task engagement is usually assessed by analyzing physiological measurements collected from subjects who are performing a task. However, physiological measurements such as Electroencephalography (EEG) vary from subject to subject. An assessment model trained for one subject may not be applicable to other subjects. We proposed a dynamic classifier selection algorithm for model individualization and compared it to other two methods: base line normalization and similarity-based model replacement. Experimental results showed that baseline normalization and dynamic classifier selection can significantly improve cross-subject engagement assessment. For complex tasks such as piloting an air plane, labeling engagement levels for pilots is challenging. Without enough labeled data, it is very difficult for traditional methods to train valid models for effective engagement assessment. This dissertation proposed to utilize deep learning models to address this challenge. Deep learning models are capable of learning valuable feature hierarchies by taking advantage of both labeled and unlabeled data. Our results showed that deep models are better tools for engagement assessment when label information is scarce. To further verify the power of deep learning techniques for scarce labeled data, we applied the deep learning algorithm to another small size data set, the ADNI data set. The ADNI data set is a public data set containing MRI and PET scans of Alzheimer\u27s Disease (AD) patients for AD diagnosis. We developed a robust deep learning system incorporating dropout and stability selection techniques to identify the different progression stages of AD patients. The experimental results showed that deep learning is very effective in AD diagnosis. In addition, we studied several imbalance learning techniques that are useful when data is highly unbalanced, i.e., when majority classes have many more training samples than minority classes. Conventional machine learning techniques usually tend to classify all data samples into majority classes and to perform poorly for minority classes. Unbalanced learning techniques can balance data sets before training and can improve learning performance

Kings Canyon to Alger Pass Pipeline Project, Project No. UTU-82322

XTO has requested their existing Temporary Use Permit (UTU-82322-01), which authorizes an existing 8 inch, surface, steel, and natural gas pipeline be converted to a permanent right-of-way grant. In addition, XTO proposes to remove a portion of the existing pipeline and to install an additional 15,075 feet of 12 inch, buried, natural gas pipe line to re-direct the flow of gas. XTO has constructed a natural gas compressor plant (Wild Horse Bench Compressor Site) on Ute Tribal land and would like to redirect the flow of gas from the Kings Canyon Area to this facility which involves the removal of a portion of pipeline and add an additional pipeline

Overall report of the ad hoc group on oil pollution incidents : Brest, France, 2-9 June 1978 & Charlottenlund, 13-15 March 1979.

Contributors: Grim Berge, Karsten H. Palmor

GASCO Production Company Proposes to Drill 6 Oil Wells from 6 Existing Well Pads

This Environmental Assessment (EA) has been prepared to analyze the potential impacts of GASCO Production Company (GASCO) oil well drilling project in the 8 Mile Flat area of Uintah County, Utah. GASCO has a valid existing right to extract mineral resources from federal leases UTU-16544, UTU-76262, UTU-75090 & UTU-78433 subject to the lease\u27s terms and conditions. The BLM oil and gas leasing program encourages development of domestic oil and gas reserves and the reduction of U.S. dependence on foreign energy sources

Novel technologies for the detection and mitigation of drowsy driving

In the human control of motor vehicles, there are situations regularly encountered wherein the vehicle operator becomes drowsy and fatigued due to the influence of long work days, long driving hours, or low amounts of sleep. Although various methods are currently proposed to detect drowsiness in the operator, they are either obtrusive, expensive, or otherwise impractical. The method of drowsy driving detection through the collection of Steering Wheel Movement (SWM) signals has become an important measure as it lends itself to accurate, effective, and cost-effective drowsiness detection. In this dissertation, novel technologies for drowsiness detection using Inertial Measurement Units (IMUs) are investigated and described. IMUs are an umbrella group of kinetic sensors (including accelerometers and gyroscopes) which transduce physical motions into data. Driving performances were recorded using IMUs as the primary sensors, and the resulting data were used by artificial intelligence algorithms, specifically Support Vector Machines (SVMs) to determine whether or not the individual was still fit to operate a motor vehicle. Results demonstrated high accuracy of the method in classifying drowsiness. It was also shown that the use of a smartphone-based approach to IMU monitoring of drowsiness will result in the initiation of feedback mechanisms upon a positive detection of drowsiness. These feedback mechanisms are intended to notify the driver of their drowsy state, and to dissuade further driving which could lead to crashes and/or fatalities. The novel methods not only demonstrated the ability to qualitatively determine a drivers drowsy state, but they were also low-cost, easy to implement, and unobtrusive to drivers. The efficacy, ease of use, and ease of access to these methods could potentially eliminate many barriers to the implementation of the technologies. Ultimately, it is hoped that these findings will help enhance traveler safety and prevent deaths and injuries to users

Development and evaluation of a smartphone-based electroencephalography (EEG) system

The aim of the study was to design, develop and evaluate a general-purpose EEG platform which integrates with a smartphone. The target specification was a system with 19 EEG channels and data stored onto the smartphone via a Wi-Fi connection. The hardware was developed using three ADS1299 integrated circuits, and the game engine, Unity, was used to develop the smartphone app. An evaluation of the system was conducted using recordings of alpha waves during periods of eye closure in participants (Bland-Altman statistical comparison with a clinical grade EEG system). The smartphone was also used to deliver time-locked auditory stimuli using an oddball paradigm to evaluate the ability of the developed system to acquire event related potentials (ERP) during sitting and walking. No significant differences were found for the alpha wave peak amplitude, frequency and area under the curve for the intra-system (two consecutive periods of alpha waves) or inter-system (developed smartphone-based EEG system versus FDA-approved system) comparisons. ERP results showed the peak amplitude of the auditory P300 component to deviant tones was significantly higher when compared to standard tones for sitting and walking activities. It is envisaged that our general-purpose EEG system will encourage other researchers to design and build their own specific versions rather than being limited by the fixed features of commercial products

Confirmation Bias Estimation from Electroencephalography with Machine Learning

Cognitive biases are known to plague human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, behavioral methods are employed to measure the level of bias in a decision. However, these measures can be hindered by a multitude of subjective factors and cannot be collected in real-time. This work investigates enhancing the current measures of estimating confirmation bias with additional behavior patterns and physiological variables to explore the viability of real-time bias detection. Confirmation bias in decisions is estimated by modeling the relationship between Electroencephalography (EEG) signals and behavioral data using machine learning methods