Mariko Funada

On an image processing of eye blinking to monitor awakening levels of human beings

The main goal of the present study was to design and examine a system to monitor awakening levels of human beings using the image processing technique to recognize eye blinking. This problem was divided into two sub-problems; to obtain images by decreasing noise and simplify the images, and to detect whether eyes were closed or opened. A filtering technique and the automatic threshold determination method were used to solve the former sub-problem and the automatic detection system was designed for the latter sub-problem. To examine the reliability of the designed system, a comparison between the result detected by the system and that detected by a sensor using electroencephalograms (EEGs) was made.

Evaluating dynamic changes of driver's awakening level by grouped /spl alpha/ waves

Describes the system of automatically detecting grouped /spl alpha/ waves of EEGs using a convolution with special weighting factors such as moving average methods. A drop of human awakening level has a close relation to appearances of grouped /spl alpha/ waves. Then using this system, it is possible to detect drivers low awakening condition and prevent traffic accidents caused by this condition. The system has two important points. The first, because actual data contain various kinds of noise, this system separates grouped /spl alpha/ waves from those. The second, the system detects this condition as soon as grouped /spl alpha/ waves appear. As the result of analyzing actual EEGs taken during driving a car, we find out the fact that intervals of grouped /spl alpha/ waves appearance fluctuate.

Discrimination in Good-Trained Brain States for Brain Computer Interface

BCI (brain computer interface) is particularly important for HCI. Some of recent results concerning BCI made a great contribution to the development of the HCI research area. In this paper we define “good-trained brain states”, and then propose a method for discriminating good-trained brain states from other states. We believe that repetitious training might be effective to human brains. Human brain reactions can be quantified by ERPs (event related potentials). We analyze the data of ERPs reflecting the brain reactions, and then discuss the effect of repetitious training to the brain states.

Comparison between event related potentials obtained by syllable recall tasks and by associative recall tasks

The final goal of this research is to establish some useful verbal communication systems between computers and persons, or between handicapped persons and normal persons. For a substantial progress toward the research goal, we investigate Event Related Potentials (ERPs for short) caused by Electroencephalograms (EEGs for short). By observing ERPs, we estimate recalled words, phrases, or sentences that may contain some homonyms or related words. In particular, we pay attention to the difference between ERPs caused by recalling a single syllable individually without other syllables and ERPs caused by recalling a syllable together with a word containing the syllable. From our observation of this difference, we believe that it may be useful to discuss the possibility of estimating a recalled word by combining ERPs caused by syllables.

Dynamic changes of human systems under a simple task of HCI

Conclusions From the analyses and considerations, the conclusions of this paper are followings; u (1) We measured I/O responses of human system, states of human systems like amounts of grouped a waves, cardiac rhythms and winking under a simple HCI task. (2) The all measured data are changing dynamically. (3) It is possible to represent the response of human systems as a function of factors indicating states of human systems. (4) Amounts of grouped a waves, cardiac rhythms, and numbers of winking are one of the good factors to determine the states of human systems. (5) A results of (4) show the importance of studies about human states to design a suitable method to future HCI. (6) A flexibility or fluctuation is one of important characteristics of future HCI.

Possibility of ECGs to improve reliability of detection system of inclining sleep stages by grouped a waves

In order to prevent traffic accidents, developing a system which find out sleepy states of drivers and warning them the dangerous state. W e have found that special EEG8 called grouped a waves appear in sleepy state of drivers, and defined the sleepy states characterized by grouped a waves as ’inclining sleep (low awake) stage‘, And then, we made a subsystem to find out inclining Sleep stages of drivers using grouped a waves. The first kind of error about the subsystem is 2% and tne second kind Of that is 25”35%. In order to improve the reliability of the system, we analyze ECG6 as detail as EEGs during driving. As the results, EEGs is more suitable to sleepy states of drivers and ECGs are also used t o improve the reliability of such a detection-subsystem. 1 . INTRODUCTION It is important to develop a system to prevent traffic accidents caused by drivers‘ dozing off. We have analyzed electroencephalograms (EEGa) during driving a car or other simple works. As the results. we find out the fact that a special EEC8 called as grouped a waves appear in most cases. Then we defined the state which is characterized by the appearance o f grouped a waves as ‘inclining sleep stage’ and made a system which automatically detects inclining sleep stage using grouped a waves‘’. The ratio which the system can not detect inclining sleep is 2% and the ratfo which the system detects non-inclining sleep stage as inclining sleep stage is about 25%-35%. Since it is required not to overlook sleepy stages to such a detection system, the system w e made satisfied such property. But we wish to refine the system more reliable, and then we measure not only EEGs but also electrocardiographs (ECCs) and analyze them. Because i t is reported that ECGs are deeply concerned with fatigue of human beings‘’. in t h e f i led of ergonomics. In this paper, we analyze both of EEGs and ECGs, observe the relations between them, and discuss the possibility of ECGs to refine the detection system of fnclining sleep stages.

Dynamic Changes of ERPs in Gestaltzerfall Phenomena: Analysis Using Multi-data Selecting and Averaging Method

In the design of BCI (Brain-Computer Interface) systems, brain signals distributed in a certain rage are often regarded as the identical signals. However, there exist some cases where such rough measurement of signals and data-collection are not good enough. Phenomena called “Gestaltzerfall” are examples of such cases. For example, Gestaltzerfall in the recognition of a letter is a phenomenon that a human does not recognize the meaning of the letter when he/she keeps watching the letter for a while. The purpose of this paper is to grasp the features of Gestaltzerfall and to understand the process getting to Gestaltzerfall by observing analysing the change of ERPs (Event-Related Potentials). From our experiments and data analysis we suggest that brain-signals are useful data to detect the variation in the pattern recognition process. We believe that such detection of the variation is applicable to the design of comfortable and universal BCI systems.

An Effective ERP Model for Brain Computer Interface

The investigation of BCI (Brain Computer Interface) is particularly interesting for HCI research. Some of recent results concerning BCI have much contributed to the progress of HCI. In this paper we propose an effective ERP model that can reduce the difference among individuals in the process of repetitive tasks. Human brain reactions are quantified by ERPs (Event Related Potentials) that reflect the change of brain reactions through repetitive tasks. We discuss a method of how to even out the difference appeared in ERPs among individuals.

Studies on Imaging Methods to Realize Effective BCI through ERPs

This paper is to clarify the usefulness of imaging methods, such as imaging figures or words, for realization of effective Brain Computer Interaction (BCI). We design some experiments in which subjects engage in imaging figures or words. Event Related Potentials (ERPs) are measured through the experiments. Some variables of ERPs are introduced for the data analysis of our experimental. We consider ERPs as vectors of various components. Using the norms of the vectors, we discuss which imaging methods are suitable.

Fundamental Studies on Effective e-Learning Using Physiology Indices

In order to apply individual learning methods to an e-learning system, we need some appropriate measures to know the quantitative evaluation for the learning progress of each individual. The ratio of the number of correct answers to the number of questions is a simple measure of the achievement of the learner. However, such a simple measure may not accurately reflect the real progress of the learner. Event Related Potentials (ERPs for short) are measured from electroencephalograms (EEGs for short). We consider that ERPs may contain meaningful information about the level of the learners achievement. We had experiments measuring ERPs of subjects learning chemical formulae on an e-learning system. We try to characterize the relation among the learners achievement, hardness of learning, and the waveforms of his ERPs. This kind of characterizations may be useful for evaluating the learners achievement.