Model-Based Separation, Detection, and Classification of Eye Movements

Wadehn, Federico; Weber, Thomas; Mack, D.; Heldt, Thomas; Loeliger, Hans-Andrea

doi:10.1109/tbme.2019.2918986

Cited by 16 publications

(13 citation statements)

References 44 publications

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“…In 2020, Wadehn et al [5] extended an established oculomotor model for horizontal eye movements with neural controller signals and a blink artefact model. On simulated data, the reconstruction error of the velocity profiles was approximately half the error obtained by the commonly employed approach of joint filtering and numerical differentiation.…”

Section: Related Workmentioning

confidence: 99%

“…A reading eye-movement model based on the average fixation times and locations across a number of subjects were first proposed in the field of cognitive psychology. Current reading eye-movement models describe the eye movements that occur during reading from the point of view of different disciplines, but the methods are generally complex, and the models require a large number of hand-crafted features [5]. To address these issues, this paper regards the reading eye-movement process as a process of labelling of the reader's fixation sequence on the text, transforming the complex reading eye-movement modelling task into a sequence labelling task in natural language processing (NLP), which is easier to model [4].…”

Section: Introductionmentioning

confidence: 99%

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A Simple Model of Reading Eye Movement Based on Deep Learning

Wang

2020

IEEE Access

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At present, an increasing amount of research is being conducted on human cognitive behaviour, and reading eye-movement modelling is a research hotspot in cognitive linguistics. However, existing reading eye-movement models are complicated and require a large number of hand-crafted features. To address these issues, this paper improves upon the fixation granularity processing mode and the regression processing mode of the traditional reading eye-movement models and proposes a reading eyemovement fixation sequence labelling method to construct a simpler model. The proposed model is based on a multi-input deep-learning neural network, which takes advantage of deep learning to reduce the number of required hand-crafted features and integrates knowledge from the field of cognitive psychology to increase its accuracy. To meet the data-size requirements of the deep-learning model, this paper also proposes a reading eye-movement data augmentation method. The experimental results show that the proposed method can describe the actual process of reading eye-movement intuitively and that the simple reading eye-movement models based on this method can obtain a similar accuracy with existing models by using fewer hand-crafted features. INDEX TERMS Eye-movement model, deep learning, sequence labelling, cognitive computing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Simple Model of Reading Eye Movement Based on Deep Learning

Wang

2020

IEEE Access

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“…Once, the eye region is localized, its status is tracked using the deep learning models like convolutional neural network (CNN) and Artificial Neural Network (ANN) as well. Among the entire deep learning model utilized for eye blink detraction, the CNN is a renowned one [17]. Although the CNN is successful in eye blink detection, it is till been considered to be a computational complexity model, owing towards its higher time consumption in training the parameters.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

Gawande,

Badotra

2022

IJACSA

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In this research work, a novel eye-blink detection model is developed. The proposed eye blink detection model is modeled by following seven major phases: (a) video-to-frame conversion, (b) Pre-processing, (c) face detection, (d) eye region localization, (e) eye landmark detection and eye status detection, (f) eye blink detection and (f) Eye blink Classification. Initially, from the collected raw video sequence (input), each individual frames are extracted in the video -to-frame conversion phase. Then, each of the frames is subjected to pre-processing phase, where the quality of the image in the frames is improved using proposed Kernel median filtering (KMF) approach. In the face detection phase, the Viola-Jones Model has been utilized. Then, from the detected faces, the eye region is localization within the proposed eye region localization phase. The proposed Eye region localization phase encapsulates two major phases: Feature extraction and landmark detection. The features like improved active shape models (I-ASMs), Local Binary pattern are extracted from the detected facial images. Then, the eye region is localization by using a new optimized Convolution neural network framework. This optimized CNN framework is trained with the extracted features (I-ASM and LBP). Moreover, to enhance the classification accuracy of eye localization, the weight of CNN is fine-tuned using a new Seagull Optimization with Enhanced Exploration (SOEE), which is the improved version of standard Seagull Optimization Algorithm (SOA). The outcome from optimized CNN framework is providing the exact location of the eye region. Once the eye region is detected, it is essential to detect the status of the eye (whether open or close). The status of the eye is detected by computing the eye aspect ratio (EAR). Then, the identified eye blinks are classified based on the computed correlation coefficient as long and short blinks. Finally, a comparative evaluation has been accomplished to validate the projected model.

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“…Our FGEM algorithm thus provides parameter estimates while simultaneously furnishing insight into the hidden ion channel conformational dynamics, given a kinetic model. Although frequently applied to parameter estimation problems in digital signal processing, FGEM algorithms have recently been applied to biomedical signal processing problems [26,27], and has not hitherto been applied to patch clamp signals.…”

Section: Introductionmentioning

confidence: 99%

A factor graph EM algorithm for inference of kinetic microstates from patch clamp measurements

Moffett,

Cui,

Thomas

et al. 2021

Preprint

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We derive a factor graph EM (FGEM) algorithm, a technique that permits combined parameter estimation and statistical inference, to determine hidden kinetic microstates from patch clamp measurements. Using the cystic fibrosis transmembrane conductance regulator (CFTR) and nicotinic acetylcholine receptor (nAChR) as examples, we perform Monte Carlo simulations to demonstrate the performance of the algorithm. We show that the performance, measured in terms of the probability of estimation error, approaches the theoretical performance limit of maximum a posteriori estimation. Moreover, the algorithm provides a reliability score for its estimates, and we demonstrate that the score can be used to further improve the performance of estimation. We use the algorithm to estimate hidden kinetic states in lab-obtained CFTR single channel patch clamp traces.

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Model-Based Separation, Detection, and Classification of Eye Movements

Cited by 16 publications

References 44 publications

A Simple Model of Reading Eye Movement Based on Deep Learning

A Simple Model of Reading Eye Movement Based on Deep Learning

Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

A factor graph EM algorithm for inference of kinetic microstates from patch clamp measurements

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