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The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%). [ABSTRACT FROM AUTHOR]

The tremendous increase in the use of video-based eye tracking has made it possible to collect eye tracking data from thousands of participants. The traditional procedures for the manual detection and classification of saccades and for trial categorization (e.g., correct vs. incorrect) are not viable for the large datasets being collected. Additionally, video-based eye trackers allow for the analysis of pupil responses and blink behaviors. Here, we present a detailed description of our pipeline for collecting, storing, and cleaning data, as well as for organizing participant codes, which are fairly lab-specific but nonetheless, are important precursory steps in establishing standardized pipelines. More importantly, we also include descriptions of the automated detection and classification of saccades, blinks, “blincades” (blinks occurring during saccades), and boomerang saccades (two nearly simultaneous saccades in opposite directions where speed-based algorithms fail to split them), This is almost entirely task-agnostic and can be used on a wide variety of data. We additionally describe novel findings regarding post-saccadic oscillations and provide a method to achieve more accurate estimates for saccade end points. Lastly, we describe the automated behavior classification for the interleaved pro/anti-saccade task (IPAST), a task that probes voluntary and inhibitory control. This pipeline was evaluated using data collected from 592 human participants between 5 and 93 years of age, making it robust enough to handle large clinical patient datasets. In summary, this pipeline has been optimized to consistently handle large datasets obtained from diverse study cohorts (i.e., developmental, aging, clinical) and collected across multiple laboratory sites.

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

Eyeblinks are the brief closures of the lid. They are accompanied by a cocontraction of the eye muscles that temporarily pulls the whole eyeball back into its socket. When blinks occur together with execution of saccadic gaze shifts, they interfere with the saccadic premotor circuit, causing these within-blink saccades to be slower than normal and also time-locked to blinks. To analyze the trajectory of within-blink saccades, subtraction of the entangled blink-related eye movement is required. Here we propose a combination of principal component analysis (PCA) and a regression model to subtract the blink-related component of the eye movement based on the respective blink metrics. We used electrooculography (EOG) to measure eye and lid movements of 12 participants who performed saccades with and without blinks. We found that within-blink saccades are slower than without-blink saccades and are tightly coupled in time to blink onset. Surprisingly, in some participants we observed large dynamic overshoots of up to 15° for saccades of only 5° amplitude. The finding of dynamic overshoots was independently confirmed by dynamic MRI for two of the participants and challenges the current view that within-blink saccades are programmed as slow, but straight, saccades. We hypothesize that the dynamic overshoots could be attributed to inhibition of omnipause neurons during blinks, the simultaneous cocontraction of extraocular muscles, or a combination of both. NEW & NOTEWORTHY This study observed that people make large dynamic overshoots when making a saccadic eye movement within a blink but their eyes are back on target by the time the eyelids are open. We used electrooculography (EOG) to measure eye movements even when the lid is down and introduced a novel procedure to subtract blink-related EOG components. These findings challenge the current view that within-blink saccades are programmed as slow but straight saccades. [ABSTRACT FROM AUTHOR]

Advancements in technology have recently made it possible to obtain various types of biometric information from humans, enabling studies on estimation of human conditions in medicine, automobile safety, marketing, and other areas. These studies have particularly pointed to eye movement as an effective indicator of human conditions, and research on its applications is actively being pursued. The devices now widely used for measuring eye movements are based on the video-oculography (VOG) method, wherein the direction of gaze is estimated by processing eye images obtained through a camera. Applying convolutional neural networks (ConvNet) to the processing of eye images has been shown to enable accurate and robust gaze estimation. Conventional image processing, however, is premised on execution using a personal computer, making it difficult to carry out real-time gaze estimation using ConvNet, which involves the use of a large number of parameters, in a small arithmetic unit. Also, detecting eye movement events, such as blinking and saccadic movements, from the inferred gaze direction sequence for particular purposes requires the use of a separate algorithm. We therefore propose a new eye image processing method that batch-processes gaze estimation and event detection from end to end using an independently designed lightweight ConvNet. This paper discusses the structure of the proposed lightweight ConvNet, the methods for learning and evaluation used, and the proposed method's ability to simultaneously detect gaze direction and event occurrence using a smaller memory and at lower computational complexity than conventional methods. [ABSTRACT FROM AUTHOR]

In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

Eye movements have been proven the most frequent of all human activities; therefore, research on a relationship between different eye movement patterns become a hotspot in human-computer interface fields. The motivation of this paper is to develop a reading auxiliary apparatus by measuring and analyzing the electrooculography signals. We first describe the saccade detection algorithm based on the wavelet packet decomposition and the derivation blink detection algorithm. Furthermore, consecutive blinks were used to control the system's working state and a magnifier, whose position is adjusted according to the results of saccade detection. Experiential results on six participants show that recognition accuracy ratio (F1 score) is 90.096%, which reveal that the proposed system has a good recognition performance on reading activity detection and analysis.

The study of eye movements has been increasing over the past decade. It is considered that eye movements, mainly saccades and blinks, provide significant information for cognitive and visual processes of the observers. Saccades and blinks are high velocity eye movements. In this paper, the automatic identification of saccades and blinks, as well as their onset and offset, is proposed based on a novel implementation of nonlinear dynamics using the Largest Lyapunov Exponent and the logarithm of the divergence. The Largest Lyapunov Exponent detection method was tested on 25,000 saccades and 2,366 blinks, detecting with high accuracy and precision both types of eye movements. The Largest Lyapunov Exponent detection method was compared against two other existing techniques for blink and saccade identification, showing advantageous performance. [ABSTRACT FROM AUTHOR]

83% of those diagnosed with Parkinson's Disease (PD) eventually progress to PD with mild cognitive impairment (PD-MCI) followed by dementia (PDD) - suggesting a complex spectrum of pathology concomitant with aging. Biomarkers sensitive and specific to this spectrum are required if useful diagnostics are to be developed that may supplement current clinical testing procedures. We used video-based eye tracking and machine learning to develop a simple, non-invasive test sensitive to PD and the stages of cognitive dysfunction. From 121 PD (45 Cognitively Normal/45 MCI/20 Dementia/11 Other) and 106 healthy controls, we collected video-based eye tracking data on an interleaved pro/anti-saccade task. Features of saccade, pupil, and blink behavior were used to train a classifier to predict confidence scores for PD/PD-MCI/PDD diagnosis. The Receiver Operator Characteristic Area Under the Curve (ROC-AUC) of the classifier was 0.88, with the cognitive-dysfunction subgroups showing progressively increased AUC, and the AUC of PDD being 0.95. The classifier reached a sensitivity of 83% and a specificity of 78%. The confidence scores predicted PD motor and cognitive performance scores. Biomarkers of saccade, pupil, and blink were extracted from video-based eye tracking to create a classifier with high sensitivity to the landscape of PD cognitive and motor dysfunction. A complex landscape of PD is revealed through a quick, non-invasive eye tracking task and our model provides a framework for such a task to be used as a supplementary screening tool in the clinic. • Structured eye-tracking produces biomarkers sensitive to PD cognitive subtypes. • Saccade, pupil, and blink reveal a complex landscape of PD spectrum. • Functional data analysis can automatically suggest features for complex signals. • ROC-AUC of 0.88 for PD classification can be achieved through machine learning. [ABSTRACT FROM AUTHOR]

Background Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. Methods The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. Results The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. Conclusion The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics. [ABSTRACT FROM AUTHOR]

Abstract: Measuring cognitive load changes can contribute to better treatment of patients, can help design effective strategies to reduce medical errors among clinicians and can facilitate user evaluation of health care information systems. This paper proposes an eye-based automatic cognitive load measurement (CLM) system toward realizing these prospects. Three types of eye activity are investigated: pupillary response, blink and eye movement (fixation and saccade). Eye activity features are investigated in the presence of emotion interference, which is a source of undesirable variability, to determine the susceptibility of CLM systems to other factors. Results from an experiment combining arithmetic-based tasks and affective image stimuli demonstrate that arousal effects are dominated by cognitive load during task execution. To minimize the arousal effect on CLM, the choice of segments for eye-based features is examined. We then propose a feature set and classify three levels of cognitive load. The performance of cognitive load level prediction was found to be close to that of a reaction time measure, showing the feasibility of eye activity features for near-real time CLM. [Copyright &y& Elsevier]

Rapid eyelid closure, or a blink, often accompanies head-restrained and head-unrestrained gaze shifts. This study examines the interactions between such gaze-evoked blinks and gaze shifts in monkeys. Blink probability increases with gaze amplitude and at a faster rate for head-unrestrained movements. Across animals, blink likelihood is inversely correlated with the average gaze velocity of large-amplitude control movements. Gaze-evoked blinks induce robust perturbations in eye velocity. Peak and average velocities are reduced, duration is increased, but accuracy is preserved. The temporal features of the perturbation depend on factors such as the time of blink relative to gaze onset, inherent velocity kinematics of control movements, and perhaps initial eye-in-head position. Although variable across animals, the initial effect is a reduction in eye velocity, followed by a reacceleration that yields two or more peaks in its waveform. Interestingly, head velocity is not attenuated; instead, it peaks slightly later and with a larger magnitude. Gaze latency is slightly reduced on trials with gaze-evoked blinks, although the effect was more variable during head-unrestrained movements; no reduction in head latency is observed. Preliminary data also demonstrate a similar perturbation of gaze-evoked blinks during vertical saccades. The results are compared with previously reported effects of reflexive blinks (evoked by air-puff delivered to one eye or supraorbital nerve stimulation) and discussed in terms of effects of blinks on saccadic suppression, neural correlates of the altered eye velocity signals, and implications on the hypothesis that the attenuation in eye velocity is produced by a head movement command. [ABSTRACT FROM AUTHOR]

Blinks and saccades cause transient interruptions of visual input. To investigate how such effects influence our perceptual state, we analyzed the time courses of blink and saccade rates in relation to perceptual switching in the Necker cube. Both time courses of blink and saccade rates showed peaks at different moments along the switching process. A peak in blinking rate appeared 1,000 ms prior to the switching responses. Blinks occurring around this peak were associated with subsequent switching to the preferred interpretation of the Necker cube. Saccade rates showed a peak 150 ms prior to the switching response. The direction of saccades around this peak was predictive of the perceived orientation of the Necker cube afterwards. Peak blinks were followed and peak saccades were preceded by transient parietal theta band activity indicating the changing of the perceptual interpretation. Precisely-timed blinks, therefore, can initiate perceptual switching, and precisely-timed saccades can facilitate an ongoing change of interpretation. [ABSTRACT FROM AUTHOR]

In this study the effect of sleep deprivation on specific components of eye and eyelid movement was investigated in a group of young and healthy subjects. The duration of sleep deprivation was 20 h. Each subject had to execute different saccade tasks: reflexive saccades, voluntary prosaccades and antisaccades. Saccade latency, number of saccade errors and blink rate during the saccade tasks were evaluated as predictors of performance decrements resulting from sleep deprivation. The present study showed no significant deterioration of latency and number of errors in the different saccade tasks. However, the blink rate was significantly higher after a night without sleep than before. It is concluded that the blink rate appears to be a promising psychophysical measurement and a more sensitive parameter than saccade performance to evaluate the effects of 20 h of sleep deprivation. These results encourage further research on this topic.Copyright © 2003 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications. [ABSTRACT FROM AUTHOR]