Browsing by Author "Lin, Ken-Li"

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Classification of driver's cognitive responses using nonparametric single-trial EEG analysis
(2007)
Lin, Chin-Teng
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Ko, Li-Wei
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Lin, Ken-Li
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Kuo, Bor-Chen
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Liang, Sheng-Fu
Accidents caused by errors and failures in human performance among traffic fatalities have a high rate causing death and become an important issue in public security. The key problem causing these car accidents is mainly because that the drivers failed to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for ongoing assessment of driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-fight experiments in a virtual-reality (VR) dynamic driving environment. Three different feature extraction methods including Nonparametric Weighted Feature Extraction (NWFE), Principlal Component Analysis (PCA), Discriminant Analysis Feature Extraction (DAFE) are applied to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data can be classified with fewer features and their classification results are compared by utilizing three different classifiers including Gaussian classifier (GC), k Nearest neighbor classification (KNNC), and Naive Bayes Classifier (NBC). Experimental results show that the successful rate of Nonparametric Weighted Feature Extraction combined with Gaussian classifier is higher more than 10% compared with other combinations. It also demonstrates the feasibility of detecting and analyzing single-trail ERP signals that represent operators' cognitive states and responses to task events.
Feature selection and combination criteria for improving accuracy in protein structure prediction
(2007-06-01)
Lin, Ken-Li
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Lin, Chun-Yuan
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Huang, Chuen-Der
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Chang, Hsiu-Ming
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Yang, Chiao-Yun
The classification of protein structures is essential for their function determination in bioinformatics. At present, a reasonably high rate of prediction accuracy has been achieved in classifying proteins into four classes in the SCOP database according to their primary amino acid sequences. However, for further classification into fine-grained folding categories, especially when the number of possible folding patterns as those defined in the SCOP database is large, it is still quite a challenge. In our previous work, we have proposed a two-level classification strategy called hierarchical learning architecture (HLA) using neural networks and two indirect coding features to differentiate proteins according to their classes and folding patterns, which achieved an accuracy rate of 65.5%. In this paper, we use a combinatorial fusion technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying various criteria in combinatorial fusion to the protein fold prediction approach using neural networks with HLA and the radial basis function network (RBFN), the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than the accuracy rate of 56.5% previously obtained by Ding and Dubchak. Our results demonstrate that data fusion is a viable method for feature selection and combination in the prediction and classification of protein structure.
Incremental Mountain Clustering Method to Find Building Blocks for Constructing Structures of Proteins
(2010-12-01)
Lin, Ken-Li
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Lin, Chin-Teng
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Pal, Nikhil R.
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電控工程研究所
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Institute of Electrical and Control Engineering
In this paper we propose an algorithm named Incremental Structural Mountain Clustering Method (ISMCM) with a view to finding a library of building blocks for reconstruction of 3-D structures of proteins/peptides. The building blocks are short structural motifs that are identified based on an estimate of local "density" of 3-D fragments computed using a measure of structural similarity. The structural similarity is computed after the best-molecular-fit alignment of pairs of fragments. The algorithm is tested on two well known benchmark data sets. Following the protocols used by other researchers, for the first data set we reconstruct a set of 71 test peptides (up to first 60 residues) whereas for the second data set we reconstruct all 143 test peptides. The ISMCM algorithm is found to successfully reconstruct the test peptides in terms of both global-fit root-mean-square (RMS) error and local-fit RMS error. The low values of local-fit RMS errors suggest that these building blocks extracted by ISMCM are good quantizers, which can represent nearby fragments quite accurately. To further assess the quality of building blocks we use two alternative graphical ways. We also use Shannon's entropy to show the structural similarity of the clusters found by our algorithm. This is important as building blocks that represent clusters with structurally similar fragments will be very effective in reconstruction. The entropic analysis reveals a very interesting fact that the secondary structure of the central residue of the fragments in a cluster is most strongly conserved (minimum entropy) over the cluster, which might be an indicator that central residue of the structural motif plays a dominant role in local folding.
Nonparametric single-trial EEG feature extraction and classification of driver's cognitive responses
(2008)
Lin, Chin-Teng
;
Lin, Ken-Li
;
Ko, Li-Wei
;
Liang, Sheng-Fu
;
Kuo, Bor-Chen
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%similar to 77%, which is over 10%similar to 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. Copyright (C) 2008 Chin-Teng Lin et al.
Structural Building Blocks Construction of Protein 3-D Structures Using a Structural Variant of Mountain Clustering Method
(2009-07-01)
Lin, Ken-Li
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Lin, Chin-Teng
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Pal, Nikhil R.
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Ojha, Sudeepta
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電控工程研究所