Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree

Wang, Shuaifang and Li, Yan and Wen, Peng and Zhu, Guohun (2014) Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree. In: 6th International Conference on Signal Processing Systems (ICSPS 2014), 8-10 Dec 2014, Dubai, UAE.

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This paper proposed a method using principle component analysis based on graph entropy (PCA-GE) and J48 decision tree on electroencephalogram (EEG) signals to predict whether a person is alcoholic or not. Analysis is performed in two stages: feature extraction and classification. The principle component analysis (PCA) chooses the optimal subset of channels based on graph entropy technique and the selected subset is classified by the J48 decision tree in Weka. K-nearest neighbor (KNN) and support vector machine (SVM) in R package are also used for comparison. Experimental results show that the proposed PCA-GE method is successful in selecting a subset of channels, which contributes to the high accuracy and efficiency in the classification of alcoholics and non-alcoholics.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 Engineering and Technology Publishing. Permanent restricted access to published version due to publisher copyright policy.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 09 Feb 2015 05:03
Last Modified: 15 Feb 2017 03:55
Uncontrolled Keywords: EEG; graph entropy; horizontal visibility graph; HVG; support vector machine; SVM; principle component analysis; PCA; J48 decision tree
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: doi:10.12720/ijsps

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