Even though retinal images of objects change their locations following each eye movement, we perceive a stable and continuous world. One possible mechanism by which the brain achieves such visual stability is to construct a craniotopic coordinate by integrating retinal and extraretinal information. There have been several proposals on how this may be done, including eye-position modulation (gain fields) of retinotopic receptive fields (RFs) and craniotopic RFs. In the present study, we investigated coordinate systems used by RFs in the lateral intraparietal (LIP) cortex and frontal eye fields (FEF) and compared the two areas. We mapped the two-dimensional RFs of neurons in detail under two eye fixations and analyzed how the RF of a given neuron changes with eye position to determine its coordinate representation. The same recording and analysis procedures were applied to the two brain areas. We found that, in both areas, RFs were distributed from retinotopic to craniotopic representations. There was no significant difference between the distributions in the LIP and FEF. Only a small fraction of neurons was fully craniotopic, whereas most neurons were between the retinotopic and craniotopic representations. The distributions were strongly biased toward the retinotopic side but with significant craniotopic shifts. These results suggest that there is only weak evidence for craniotopic RFs in the LIP and FEF, and that transformation from retinotopic to craniotopic coordinates in these areas must rely on other factors such as gain fields.
Animals ; Macaca ; Visual Fields ; Frontal Lobe/physiology* ; Eye Movements ; Brain

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
Animals ; Support Vector Machine ; Whales ; Eye Movements ; Algorithms

The eye-computer interaction technology based on electro-oculogram provides the users with a convenient way to control the device, which has great social significance. However, the eye-computer interaction is often disturbed by the involuntary eye movements, resulting in misjudgment, affecting the users' experience, and even causing danger in severe cases. Therefore, this paper starts from the basic concepts and principles of eye-computer interaction, sorts out the current mainstream classification methods of voluntary/involuntary eye movement, and analyzes the characteristics of each technology. The performance analysis is carried out in combination with specific application scenarios, and the problems to be solved are further summarized, which are expected to provide research references for researchers in related fields.
Computers ; Electrooculography/methods* ; Eye Movements ; Movement

Biomechanical Phenomena ; Eye Movements ; Neurons/physiology* ; Pursuit, Smooth ; Saccades

Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
Algorithms ; Discriminant Analysis ; Eye Movements ; Humans ; Markov Chains ; Normal Distribution ; Probability

Objective To investigate the application value of eye tracking in lie detection. Methods The 40 subjects were randomly divided into two groups. The pupil diameter, fixation duration, points of fixation and blink frequency of the subjects in the experimental group in observing target stimulation and non-target stimulation were recorded with eye tracker after they accomplished the mock crime. The eye movement parameters of subjects in the control group were directly collected. The differences in eye movement parameters of the experimental group and the control group in observing target stimulation and non-target stimulation were analyzed by t-test. Pearson coefficient analysis of correlation between eye movement parameters that had differences was conducted. The effectiveness of eye movement parameters to distinguish between the experimental group and the control group was calculated by the receiver operator characteristic （ROC） curve. Results Participants from the experimental group had shorter average pupil diameter, longer average fixation duration and fewer fixation points （P<0.05）, but the differences in blink frequency had no statistical significance. The differences in the above indicators of the control group in observing target stimulation and non-target stimulation had no statistical significance. The average fixation duration showed a negative correlation with fixation points （r=-0.255, P<0.05）; the average fixation duration showed a negative correlation with average pupil diameter （r=-0.218, P<0.05）; the fixation points showed a positive correlation with average pupil diameter （r=0.09, P<0.05）. The area under the curve of average pupil diameter, average fixation duration and fixation points was 0.603, 0.621 and 0.580, respectively. Conclusion The average pupil diameter, average fixation duration and fixation points obtained by the eye tracker under laboratory conditions can be used to detect lies.
Algorithms ; Eye Movements ; Humans ; Lie Detection ; Pupil ; Time Factors

Objective To analyze the differences in accuracy of different eye movement parameters in distinguishing the cooperation and non-cooperation during image completion test of patients with mental disorders caused by craniocerebral trauma. Methods One hundred and forty cases of patients with mental disorders caused by craniocerebral trauma who took psychiatric impairment assessments were collected. The 21 pictures from "image completion" of Wechsler intelligence test were used as stimulating pictures, then divided into cooperation group and non-cooperation group according to binomial forced-choice digit memory test and expert opinions. The eye movement parameters of research subjects during completion of images were obtained by the SMI eye-tracker. The accuracy of eye movement parameters in distinguishing the cooperation or non-cooperation of patients with mental disorders caused by craniocerebral trauma in psychiatric impairment assessments were evaluated by the ROC curve. Results During the process of the image completion test, the area under curve （AUC） value of frequency of blink, frequency of fixation, pupil size, frequency of saccade, latency of saccade, average acceleration of saccade, the average and peak longitudinal velocity of saccade was above 0.5. When it comed to a specific stimulating picture, the AUC value of frequency of blink in looking at a specific stimulating picture could be above 0.8, and the AUC value of X axis diameter of pupil size could be above 0.7. Conclusion The accuracy of eye movement parameters in distinguishing the cooperation or disguise of patients with mental disorders caused by craniocerebral trauma is related with the stimulating picture. The accuracy of frequency of blink in distinguishing cooperation and non-cooperation is better than that of other eye movement parameters.
Blinking ; Eye Movements ; Humans ; Intelligence Tests

A 31-year-old woman presented with the very unusual symptom of being able to hear the movement of her eyeballs in her left ear: “I can hear my eyeballs move!” She initially described hearing a recurrent “swishing” sound that would occur intermittently. She eventually realized that its occurrence coincided with eyeball movement. In the eight months’ duration of her symptom, she had been unable to obtain a diagnosis from physicians whom she consulted and had even been referred for psychiatric evaluation and treatment. An otolaryngologist whom she consulted had a standard pure tone audiometric examination done, and this showed normal hearing acuity in both ears. A Magnetic Resonance Imaging (MRI) of the inner ear and brain likewise showed no abnormalities. Due to the peculiarity of the patient’s complaint, the otolaryngologist consulted with a neurotologist who suspected the presence of a semicircular canal dehiscence. A computerized tomographic imaging study of the temporal bone confirmed the presence of a left superior semicircular canal dehiscence syndrome.
Semicircular Canal Dehiscence ; Semicircular Canals ; Eye Movements

Luteolin, a widespread flavonoid, has been known to have neuroprotective activity against various neurologic diseases such as epilepsy, and Alzheimer’s disease. However, little information is available regarding the hypnotic effect of luteolin. In this study, we evaluated the hypnotic effect of luteolin and its underlying mechanism. In pentobarbital-induced sleeping mice model, luteolin (1, and 3 mg/kg, p.o.) decreased sleep latency and increased the total sleep time. Through electroencephalogram (EEG) and electromyogram (EMG) recording, we demonstrated that luteolin increased non-rapid eye movement (NREM) sleep time and decreased wake time. To evaluate the underlying mechanism, we examined the effects of various pharmacological antagonists on the hypnotic effect of luteolin. The hypnotic effect of 3 mg/kg of luteolin was not affected by flumazenil, a GABAA receptor-benzodiazepine (GABAAR-BDZ) binding site antagonist, and bicuculine, a GABAAR-GABA binding site antagonist. On the other hand, the hypnotic effect of 3 mg/kg of luteolin was almost completely blocked by caffeine, an antagonist for both adenosine A1 and A2A receptor (A1R and A2AR), 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX), an A1R antagonist, and SCH-58261, an A2AR antagonist. From the binding affinity assay, we have found that luteolin significantly binds to not only A1R but also A2AR with IC₅₀ of 1.19, 0.84 μg/kg, respectively. However, luteolin did not bind to either BDZ-receptor or GABAAR. From these results, it has been suggested that luteolin has hypnotic efficacy through A1R and A2AR binding.
Adenosine ; Animals ; Binding Sites ; Caffeine ; Electroencephalography ; Epilepsy ; Eye Movements ; Flumazenil ; Hand ; Hypnotics and Sedatives ; Luteolin ; Mice ; Receptor, Adenosine A1 ; Receptor, Adenosine A2A ; Sleep Initiation and Maintenance Disorders

No abstract available.
Eye Movements ; Rhabdomyoma