Minoru Nakayama

Frequency analysis of task evoked pupillary response and eye-movement

This paper describes the influence of eye blinks on frequency analysis and power spectrum difference for task-evoked pupillography and eye-movement during an experiment which consisted of ocular following tasks and oral calculation tasks with three levels of task difficulty: control, 1×1,and 1×2 digit oral calculation.The compensation model for temporal pupil size based on MLP (multi layer perceptron) was trained to detect a blink and to estimate pupil size by using blinkless pupillary change and artificial blink patterns. The PSD (power spectrum density) measurements from the estimated pupillography during oral calculation tasks show significant differences, and the PSD increased with task difficulty in the area of 0.1 - 0.5Hz and 1.6 - 3.5Hz, as did the average pupil size.The eye-movement during blinks was corrected manually, to remove irregular eye-movements such as saccades. The CSD (cross spectrum density) was achieved from horizontal and vertical eye-movement coordinates. Significant differences in CSDs among experimental conditions were examined in the area of 0.6 - 1.5 Hz. These differences suggest that the task difficulty affects the relationship between horizontal and vertical eye-movement coordinates in the frequency domain.

Influence of blink on pupillary indices

Pupil Unrest Index (PUI) is often used clinically for assessing exhaustion and sleepiness. Although pupil size measuring, which is based on the image processing of the eye, can be affected by blink, this influence has not been sufficiently considered for PUI evaluation. Hence, this paper seeks to examine the relationship between blink and indices of the pupil. The result shows that blink rate has a significant effect on the indices of the pupil. The difference in pupillary indices across experimental conditions was revealed by the appropriate estimation of pupil size during blink.

Evaluation of a multimedia learning exercise using oculo-motors

A lot of multimedia learning materials and software have been developed as learning tools. Their usability for learning has not often been discussed, however. The issue of system usabilty is often considered regarding various other processes, but learning materials should also be evaluated. The indices of oculo-motors can be used for usability tests [Nakayama et al. 2002], so they may also indicate the usability of learning materials. To examine the feasibility of using oculo-motors to evaluate multimedia learning materials, an exercise was tested under experimental conditions.

Estimation of certainty for multiple choice tasks using features of eye-movements

To determine the performance of estimating the degree of strength of belief (SOB) of responses using eye-movements, the features of eye-movements were extracted while subjects answered questions and reviewed their own responses to multiple choice tasks. The estimation was conducted using Support Vector Machines (SVM) with features of eye-movements between two successive fixations. In the results, the overall estimation performances were significant when the prediction was calculated using certain combinations of features for answering and reviewing sessions respectively. In comparing feature combinations of eye-movements for significant estimations between answering and reviewing sessions, significant combinations components of features between the two sessions did not coincide.

Assessing usability with eye-movement frequency analysis

Eye-movements can be an index of usability, though there is no significant relationship between a subjective usability score and indices of eye-movements, however [Nakayama and Katsukura 2007]. A possible reasons for this is the shortness of the observational duration. As another approach, power spectrum density (PSD) and cross spectrum density (CSD) of eye-movements can be used as an index of mental workload [Nakayama and Shimizu 2004]. This paper addresses the possibility of assessing usability using frequency analysis of eye-movements.

Face identification performance using facial expressions as perturbation

The paper presents improvements in face identification performance using synthesized images as a perturbation method. Three facial expression features, smiles, anger and screams, were extracted from images of actual facial expression using the eigenspace method. Synthesized facial images based on these features were added to learning data of a personal identification model using support vector machines (SVM). The performance of this model was significantly higher than that of a model trained without facial expression images, but significantly lower than that of a model using actual expression images. The results suggest that identification performance also depends significantly on facial expression.