Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions
(Conference Presentation)

Thien Nguyen; Sangtae Ahn; Hyojung Jang; Sung C Jun; Jae G. Kim

doi:10.1117/12.2208556

26 April 2016 Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions (Conference Presentation)

Thien Nguyen, Sangtae Ahn, Hyojung Jang, Sung C Jun, Jae G. Kim

Author Affiliations +

Proceedings Volume 9690, Clinical and Translational Neurophotonics; Neural Imaging and Sensing; and Optogenetics and Optical Manipulation; 969003 (2016) https://doi.org/10.1117/12.2208556
Event: SPIE BiOS, 2016, San Francisco, California, United States

Abstract

Driver’s condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver’s status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher’s linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.

Conference Presentation

Citation Download Citation

Thien Nguyen, Sangtae Ahn, Hyojung Jang, Sung C Jun, and Jae G. Kim "Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions (Conference Presentation)", Proc. SPIE 9690, Clinical and Translational Neurophotonics; Neural Imaging and Sensing; and Optogenetics and Optical Manipulation, 969003 (26 April 2016); https://doi.org/10.1117/12.2208556

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KEYWORDS

Electroencephalography

Brain

Classification systems

Safety

Current controlled current source

Library classification systems

Neurophotonics

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