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EEG・ECG・NIRSを用いた注意レベルの分類のための瞳孔測定によるラベリング手法

ゼニファ, ファディラ FADILLA, ZENNIFA 九州大学

2020.03.23

概要

There are numerous methods to evaluate attention levels such as observation, self-assessment, and objective performance. This study aims to propose a new labeling method for attention levels detection by using parameter settings of pupillometry. This parameter setting then would be applied as data labeling in supervised machine learning toward EEG-ECG-NIRS.

To develop parameter settings of attention level evaluation, this study investigated the reaction of blink rates and pupillometry toward attention level based on self-assessment during cognitive tasks. My result showed there is no significant differences (P>0.05) in blink rates toward attention level within 10 seconds. On the other hand, pupillometry in low attention showed significant differences in pupillometry in the last 4 seconds cognitive tasks (P<0.05). After that, I calculated the distribution fit of pupillometry reaction in the attention level of all participants and plot the critical point of pupillometry data in 10 seconds and 4 seconds. After doing several experimental procedures, I chose parameter setting with a percentage of error of less than 15% and a different error 35 % compare with self assesment as future labeling method. Parameter setting which has been selected is when z-score within a specific range (-0.965 ≤ pupil ≤ 1.014) as high attention, other that range, will be classified as low attention.

Furthermore, I applied my labeling method for another physiological signal such as electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS). Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for attention level detection have been proposed. However, the results were either unsatisfactory or required many channels. In this study, I introduce the implementation of an EEG-ECG-NIRS for attention level detection. I used two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to detect attention level during backward digit span, forward digit span and arithmetic. High attention will be labelled to data which has pupillometry z-score within specific range (-0.965 ≤ pupil ≤ 1.014) and another that range, will be classified as low attention. By using CFS+kNN algorithm, my result showed the accuracy system of EEG-ECG-NIRS (83.33± 5.95%) has the highest accuracy compare with EEG (81.90± 4.69%), ECG (82.51±3.57%), NIRS (78.37±7.12%). Algorithm CFS+kNN also shown highest performance compare with other methods such as CFS+SVM (55.49± 27.89%), kNN (80.84± 3.88%) and SVM (55.88± 13.14%)

In summary, in this study, I established new parameter settings for evaluating attention level by using pupillometry and apply the parameter settings into EEG-ECG-NIRS to evaluate the EEG-ECG-NIRS performance, comparing with standalone system.

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