The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Analysis of Dimensionality Reduction Methods Through Epileptic EEG Feature Selection for Machine Learning in BCI

BCI에서 기계 학습을 위한 간질 뇌파 특징 선택을 통한 차원 감소 방법 분석

  • 양통 (전남대학교 컴퓨터공학전공) ;
  • ;
  • 임창균 (전남대학교 컴퓨터공학전공)
  • Received : 2018.10.01
  • Accepted : 2018.12.15
  • Published : 2018.12.31
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Abstract

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

지금까지 뇌파(Electroencephalography - EEG)는 뇌전증 진단 및 치료를 위한 가장 중요하고 편리한 방법이었다. 그러나 뇌전증 뇌파 신호의 파형 특성은 매우 약하고 비 정지 상태이며 배경 노이즈가 강하기 때문에 식별하기가 어렵다. 이 논문에서는 간질 뇌파의 특징 선택을 통한 차원 감소를 통한 분류 방법의 효과를 분석한다. 우리는 차원 감소를 위해 주 요소 분석, 커널 요소 분석, 선형 판별 분석 방법을 사용하였다. 차원 감소방법의 성능 분석을 위해 Support Vector Machine: SVM), Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR), Random Forest(: RF) 분류 방법들을 사용해 평가하였다. 실험 결과에 따르면, PCA는 SVM, LR 및 K-NN에서 75% 정확도를 나타냈다. KPCA는 SVM과 K-KNN에서 85%의 성능을 보였으며 LDA는 K-NN를 이용했을 때 100 %의 정확도 보여주었다. 따라서 LDA를 이용한 차원 감소가 뇌전증 EEG 신호에 대한 최고의 분류 결과 보여주었다.

Keywords

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Fig. 1 Electroencephalograms in normal brain for two 3s continuous EEG Signals. It depicts the EEG of the brain under normal condition which is chaotic and irregular.

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Fig. 2 Electroencephalograms in patients with epilepsy for two 3s continuous EEG Signals. It depicts the EEG of the brain during Epilepsy attack. It has higher amplitude and rhymic pattern than under normal condition.

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Fig. 3 System structure. The system consist of two major parts:- Dimensionality reduction methods and classifiers. The dimensionality reduction methods include PCA, KPCA and LDA while the classifiers used include SVM, LR, KNN, DT, and R

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Fig. 4 General flow chart. The features from the dataset are extracted using discrete wavelet transform then feature selection were conducted using three dimensionality reduction methods before passing the data to classifiers.

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Fig. 5 Comparison of the performance of dimensionality reduction methods and descret wave transformation in classification

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Fig. 6 Comparing epileptic EEG classification results with three dimension reduction methods

Table 1. The composition of training set sample points and test set sample points

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Table 2. Accuracy of dimensionality reduction methods with classifiers

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References

  1. L. Rrambaiolli, N. Spolaor, and A. Corena, "Feature selection before EEG classification supports the diagnosis of Alzheimer's disease," Clinical Neurophysiology, vol. 128, no. 10, 2017, pp. 2058-2067. https://doi.org/10.1016/j.clinph.2017.06.251
  2. S. Moshe, E. Perucca, P. Ryvlin, and T. Tomson, "Epilepsy: new advances," The Lancet, vol. 385, Issue 9971, Mar. 2015, pp. 884-898. https://doi.org/10.1016/S0140-6736(14)60456-6
  3. J. Zhao, W. Zhou, K. Liu, and D. Cai, "EEG Signal Classification Method Based on Svm And Wavelet Analysis,"Computer Applications & Software, vol. 28, no. 5, 2011, pp. 114-116. https://doi.org/10.3969/j.issn.1000-386X.2011.05.034
  4. J. Jo "Drone Based Sensor Network Scenario for the Efficient Pedestrian's EEG Signal Transmission", J. of the Korea Institute of Electronic Communication Sciences, Sept. vol. 11, no. 9, 2016, pp. 923-928. https://doi.org/10.13067/JKIECS.2016.11.9.923
  5. Y. Jang "Analysis of Concentration-Related EEG Component Due to Smartphone", J. of the Korea Institute of Electronic Communication Sciences, vol. 11, no. 7, July 2016, pp. 717-722. https://doi.org/10.13067/JKIECS.2016.11.7.717
  6. G. Kumar and P. Bhatia, "A Detailed Review of Feature Extraction in Image Processing Systems," Fourth International Conference on Advanced Computing & Communication Technologies, Rohtak, India, Feb. 2014, pp. 5-12.
  7. R. Davidson, D. Jackson, and C. Larson, Human electroencephalography, Handbook of Psychophysiology, New York. Cambridge University Press, 2000.
  8. M. Nurujjaman, R. Narayanan, and A. Sekar Iyengar, "Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients," Nonlinear Biomedical Physics, vol. 3, no. 1, 2009, pp. 1-5. https://doi.org/10.1186/1753-4631-3-1
  9. R. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, and C. Elger, "Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state," Physical Review E, vol. 64, 061907, Nov. 2001, pp. 1-8. view
  10. Q. Cai , H. Chen, and L. Xie, "Analysis of EEG Based on Improvement Wavelet Transform," Computer Technology & Development, 2008.
  11. J. Costa, P. Da-Silva, R. Almeida, and A. Infantosi, "Validation in Principal Components Analysis Applied to EEG Data," Computational and Mathematical Methods in Medicine, vol. 2014, Sep.2014, pp. 1-10.
  12. L. Cao, K. Chua, K. Chongc, H. Lee, and Q. M. Gu, "A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine," Neurocomputing, vol. 55, issues 1-2, Sept. 2003, pp. 321-336. https://doi.org/10.1016/S0925-2312(03)00433-8
  13. A. Subasi and M. I. Gursoyb, "EEG signal classification using PCA, ICA, LDA and support vector machines," Expert Systems with Applications, vol. 37, issue 12, Dec. 2010, pp. 8659-8666. https://doi.org/10.1016/j.eswa.2010.06.065