JAIST Repository
https://dspace.jaist.ac.jp/Title

脳機能信号と行動生理信号を組み合わせたBrainMachine Interface(BMI)の提案Author(s)渡邊, 大輔CitationIssue Date

2016‑03

TypeThesis or Dissertation

Text version

authorURL

http://hdl.handle.net/10119/13649

RightsDescription

Supervisor:田中宏和, 情報科学研究科, 修士

Japan Advanced Institute of Science and Technology

Abstract
Brain Machine Interface (BMI) tools such as functional magnetic resonance imaging (fMRI),
Magnetoencephalography(MEG), and electroencephalography (EEG) are conventional methods used tomeasure signals of brain functioning. However, the cost of measurement and the time and effort required forjust one scan are drawbacks for fMRI and MEG. On the other hand, EEG is widely used in BMI research due
to its low measurement cost and simplicity. Nonetheless, a shortcoming of EEG is that it captures extremely
faint brain waves. Due to this, in order to make a judgment from the brain wave measurements, multiple trials
must be conducted. This is a drawback in that, even making a simple binary judgment requires processing time.
An additional flaw of EEG is that, as noise from slight body movements, like vibration or blinking, can easily
cause the signal to disappear, there is a low percentage of correct answers when making a judgment from thebrain wave measurements. Various approaches are being used in order to resolve these challenges, however
solutions for the above-mentioned problems have yet to be reached. Meanwhile, there is also research being
conducted using the behavioral and physiological signals of pupil size, eye movement, and heart rate to makejudgments. There has also been research demonstrating that when subjects are given a working memory
challenge, the larger the amount of information being stored becomes, the larger the opening of the pupil
becomes. Both behavioral physiological signals and brain waves are characterized by a relation with cognitionindicators such as judgment and memory capacity, and compared to EEG, it is possible to obtain highly
accurate readings against noise. In addition, for the same working memory challenge, there is research showingthat the more memory capacity used, the larger the amplitude of the brain waves becomes, and it is predictedthat there is a correlation between pupil size and brain wave amplitude. In the present research, in order toresolve the problems with BMI using EEG, we will propose a “brain wave and pupil simultaneousmeasurement system” in which brain waves and pupil size are measured simultaneously, and the brain wave
data is supplemented with the pupil data. Specifically, we will measure the pupil size change and brain waves
simultaneously when conducting a working memory challenge to make a binary judgment. The measured data
will be optimized with principal component analysis, and a Support Vector Machine will be used to study thetwo-choice pattern. With respect to the results of that study, new test data will be released and the result of thetwo-choice option will be hypothesized. Regarding the correct answer rate of the results hypothesis, we willverify that the correct answer rate from analysis of the EEG alone can be improved by adding the pupil
information.