Nonparametric single-trial EEG feature extraction and classification of driver's cognitive responses

I. Fang Chung*, Chin Teng Lin, Ken Li Lin, Li-Wei Ko, Sheng Fu Liang, Bor Chen Kuo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


We proposed an electroencephalographic (EEG) signal analysis approach to investigate the driver's cognitive response to traffic-light experiments in a virtual-reality-(VR-) based simulated driving environment. EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), and linear discriminant analysis (LDA), which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data could be classified with fewer features and their classification results were compared by utilizing two different classifiers including k nearest neighbor classification (KNNC) and naive bayes classifier (NBC). Experimental data were collected from 6 subjects and the results show that NWFE+NBC gives the best classification accuracy ranging from 71%∼77%, which is over 10%∼24% higher than LDA+KNN1. It also demonstrates the feasibility of detecting and analyzing single-trial EEG signals that represent operators' cognitive states and responses to task events.

Original languageEnglish
Article number849040
JournalEurasip Journal on Advances in Signal Processing
StatePublished - 30 Jun 2008

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