scholarly journals Human-level saccade detection performance using deep neural networks

2019 ◽  
Vol 121 (2) ◽  
pp. 646-661 ◽  
Author(s):  
Marie E. Bellet ◽  
Joachim Bellet ◽  
Hendrikje Nienborg ◽  
Ziad M. Hafed ◽  
Philipp Berens
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Convolutional Neural Network ◽  
Eye Movement ◽  
Visual Processing ◽  
Performance Metrics ◽  
Visual Space ◽  
Eye Tracker ◽  
Wide Range ◽  
Training Examples

Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network to automatically detect saccades at human-level accuracy and with minimal training examples. Our algorithm surpasses state of the art according to common performance metrics and could facilitate studies of neurophysiological processes underlying saccade generation and visual processing. NEW & NOTEWORTHY Detecting saccades in eye movement recordings can be a difficult task, but it is a necessary first step in many applications. We present a convolutional neural network that can automatically identify saccades with human-level accuracy and with minimal training examples. We show that our algorithm performs better than other available algorithms, by comparing performance on a wide range of data sets. We offer an open-source implementation of the algorithm as well as a web service.

scholarly journals Human-level saccade detection performance using deep neural networks

2018 ◽  
Author(s):  
Marie E. Bellet ◽  
Joachim Bellet ◽  
Hendrikje Nienborg ◽  
Ziad M. Hafed ◽  
Philipp Berens
Keyword(s):  
Eye Movements ◽  
Cognitive Processes ◽  
Visual Processing ◽  
Performance Metrics ◽  
Visual Space ◽  
Neural Mechanisms ◽  
Eye Tracker ◽  
Noise Levels ◽  
Performance Accuracy ◽  
Level Performance

Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network (CNN) to automatically detect saccades at human-level performance accuracy. Our algorithm surpasses state of the art according to common performance metrics, and will facilitate studies of neurophysiological processes underlying saccade generation and visual processing.

scholarly journals Is the eye-movement field confused about fixations and saccades? A survey among 124 researchers

2018 ◽  
Vol 5 (8) ◽  
pp. 180502 ◽  
Author(s):  
Roy S. Hessels ◽  
Diederick C. Niehorster ◽  
Marcus Nyström ◽  
Richard Andersson ◽  
Ignace T. C. Hooge
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Coordinate System ◽  
Eye Movement ◽  
Eye Tracker ◽  
New Methods ◽  
Research Fields ◽  
Wide Range ◽  
Movement Field ◽  
Mobile Eye Tracking

Eye movements have been extensively studied in a wide range of research fields. While new methods such as mobile eye tracking and eye tracking in virtual/augmented realities are emerging quickly, the eye-movement terminology has scarcely been revised. We assert that this may cause confusion about two of the main concepts: fixations and saccades. In this study, we assessed the definitions of fixations and saccades held in the eye-movement field, by surveying 124 eye-movement researchers. These eye-movement researchers held a variety of definitions of fixations and saccades, of which the breadth seems even wider than what is reported in the literature. Moreover, these definitions did not seem to be related to researcher background or experience. We urge researchers to make their definitions more explicit by specifying all the relevant components of the eye movement under investigation: (i) the oculomotor component: e.g. whether the eye moves slow or fast; (ii) the functional component: what purposes does the eye movement (or lack thereof) serve; (iii) the coordinate system used: relative to what does the eye move; (iv) the computational definition: how is the event represented in the eye-tracker signal. This should enable eye-movement researchers from different fields to have a discussion without misunderstandings.

scholarly journals Visual space curves before eye movements

2021 ◽  
Author(s):  
Ifedayo-Emmmanuel Adeyefa-Olasupo
Keyword(s):  
Eye Movements ◽  
Visual System ◽  
Eye Movement ◽  
Visual Processing ◽  
Mechanical Model ◽  
Visual Space ◽  
Visual Sensitivity ◽  
Visual World ◽  
Space Curves ◽  
Transient Error

Despite the incessant retinal disruptions that necessarily accompany eye movements, our percept of the visual world remains continuous and stable—a phenomenon referred to as spatial constancy. How the visual system achieves spatial constancy remains unclear despite almost four centuries worth of experimentation. Here I measured visual sensitivity at geometrically symmetric locations, observing transient sensitivity differences between them where none should be observed if cells that support spatial constancy indeed faithfully translate or converge. These differences, recapitulated by a novel neurobiological mechanical model, reflect an overriding influence of putative visually transient error signals that curve visual space. Intermediate eccentric locations likely to contain retinal disruptions are uniquely affected by curved visual space, suggesting that visual processing at these locations is transiently turned off before an eye movement, and with the gating off of these error signals, turned back on after an eye-movement— a possible mechanism underlying spatial constancy.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

scholarly journals A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Chong-Bin Tsai ◽  
Wei-Yu Hung ◽  
Wei-Yen Hsu
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Low Cost ◽  
Saccadic Eye Movements ◽  
Slow Phase ◽  
Eye Tracker ◽  
Effective System ◽  
Tracking Device ◽  
Traditional Approaches

Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a “slow phase (SP)” with eye movements in the same direction as the movement of the pattern and a “fast phase (FP)” with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods & Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.

Eye Movements as a Means to Evaluate Operation Interface of Flat Knitting Machine

2014 ◽  
Vol 607 ◽  
pp. 664-668
Author(s):  
Zhi Hui Liu ◽  
Sheng Ze Wang ◽  
Qiong Shen ◽  
Jia Jun Feng
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Objective Evaluation ◽  
Layout Design ◽  
Eye Tracker ◽  
Heat Maps ◽  
Machine Operation

This study investigates the characteristics of eye movements by operating flat knitting machine. For the objective evaluation purpose of the flat knitting machine operation interface, we arrange participants finish operation tasks on the interface, then use eye tracker to analyze and evaluate the layout design. Through testing of the different layout designs, we get fixation sequences, the count of fixation, heat maps, and fixation length. The results showed that the layout design could significantly affect the eye-movement, especially the fixation sequences and the heat maps, the count of fixation and fixation length are always impacted by operation tasks. Overall, data obtained from eye movements can not only be used to evaluate the operation interface, but also significantly enhance the layout design of the flat knitting machine.

scholarly journals PedNet: A Spatio-Temporal Deep Convolutional Neural Network for Pedestrian Segmentation

2018 ◽  
Vol 4 (9) ◽  
pp. 107 ◽  
Author(s):  
Mohib Ullah ◽  
Ahmed Mohammed ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Performance Metrics ◽  
State Of The Art ◽  
Temporal Information ◽  
Feature Maps ◽  
Current Frame ◽  
Low Level ◽  
Art Methods ◽  
Spatio Temporal

Articulation modeling, feature extraction, and classification are the important components of pedestrian segmentation. Usually, these components are modeled independently from each other and then combined in a sequential way. However, this approach is prone to poor segmentation if any individual component is weakly designed. To cope with this problem, we proposed a spatio-temporal convolutional neural network named PedNet which exploits temporal information for spatial segmentation. The backbone of the PedNet consists of an encoder–decoder network for downsampling and upsampling the feature maps, respectively. The input to the network is a set of three frames and the output is a binary mask of the segmented regions in the middle frame. Irrespective of classical deep models where the convolution layers are followed by a fully connected layer for classification, PedNet is a Fully Convolutional Network (FCN). It is trained end-to-end and the segmentation is achieved without the need of any pre- or post-processing. The main characteristic of PedNet is its unique design where it performs segmentation on a frame-by-frame basis but it uses the temporal information from the previous and the future frame for segmenting the pedestrian in the current frame. Moreover, to combine the low-level features with the high-level semantic information learned by the deeper layers, we used long-skip connections from the encoder to decoder network and concatenate the output of low-level layers with the higher level layers. This approach helps to get segmentation map with sharp boundaries. To show the potential benefits of temporal information, we also visualized different layers of the network. The visualization showed that the network learned different information from the consecutive frames and then combined the information optimally to segment the middle frame. We evaluated our approach on eight challenging datasets where humans are involved in different activities with severe articulation (football, road crossing, surveillance). The most common CamVid dataset which is used for calculating the performance of the segmentation algorithm is evaluated against seven state-of-the-art methods. The performance is shown on precision/recall, F 1 , F 2 , and mIoU. The qualitative and quantitative results show that PedNet achieves promising results against state-of-the-art methods with substantial improvement in terms of all the performance metrics.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

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