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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46489

Title: Driver's cognitive state classification toward brain computer interface via using a generalized and supervised technology
Authors: Chun-Hsiang Chuang
Pei-Chen Lai
Li-Wei Ko
Bor-Chen Kuo
Chin-Teng Lin
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Feature extraction;Driver circuits;Electroencephalography;Classification algorithms;Brain modeling;Principal component analysis;Support vector machines
Date: 2010-07
Issue Date: 2018-05-21T06:07:05Z
Publisher: Neural Networks (IJCNN), The 2010 International Joint Conference on
Abstract: Abstract:
Growing numbers of traffic accidents had become a serious social safety problem in recent years. The main factor of the high fatalities was the obvious decline of the driver's cognitive state in their perception, recognition and vehicle control abilities while being sleepy. The key to avoid the terrible consequents is to build a detecting system for ongoing assessment of driver's cognitive state. A quickly growing research, brain-computer interface (BCI), offers a solution offering great assistance to those who require alternative communicatory and control mechanisms. In this study, we propose an alertness/drowsiness classification system based on investigating electroencephalographic (EEG) brain dynamics in lane-keeping driving experiments in a virtual reality (VR) driving environment with a motion platform. The core of the classification system is composed of dimension reduction technique and classifier learning algorithm. In order to find the suitable method for better describing the data structure, we explore the performances using different feature extraction and feature selection methods with different classifiers. Experiment results show that the accuracy is over 80% in most combinations and even near 90% under Principal Component Analysis (PCA) and Nonparametric Weighted Feature Extraction (NWFE) going with Gaussian Maximum Likelihood classifier (ML) and k-Nearest-Neighbor classifier (kNN), respectively. In addition, this developed classification system can also solve the individual brain dynamic differences caused from different subjects and overcome the subject dependent limitation. The optimized solution with better accuracy performance out of all combinations can be considered to implement in the kernel brain-computer interface.
Relation: pp.1-7
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46489
Appears in Collections:[資訊工程學系] 演講及研討會

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