Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree
Paper
Paper/Presentation Title | Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree |
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Presentation Type | Paper |
Authors | Wang, Shuaifang (Author), Li, Yan (Author), Wen, Peng (Author) and Zhu, Guohun (Author) |
Editors | Zhang, Teresa |
Journal or Proceedings Title | Proceedings of the 6th International Conference on Signal Processing Systems (ICSPS 2014) |
Number of Pages | 6 |
Year | 2014 |
Place of Publication | Rowland Heights, CA. United States |
Digital Object Identifier (DOI) | https://doi.org/10.12720/ijsps |
Web Address (URL) of Paper | http://www.icsps.org/ |
Conference/Event | 6th International Conference on Signal Processing Systems (ICSPS 2014) |
Event Details | 6th International Conference on Signal Processing Systems (ICSPS 2014) Event Date 08 to end of 10 Dec 2014 Event Location Dubai, United Arab Emirates |
Abstract | 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. |
Keywords | EEG; graph entropy; horizontal visibility graph; HVG; support vector machine; SVM; principle component analysis; PCA; J48 decision tree |
ANZSRC Field of Research 2020 | 460902. Decision support and group support systems |
469999. Other information and computing sciences not elsewhere classified | |
320602. Medical biotechnology diagnostics (incl. biosensors) | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | University of Southern Queensland |
https://research.usq.edu.au/item/q2w96/analyzing-eeg-signals-using-graph-entropy-based-principle-component-analysis-and-j48-decision-tree
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