Selection of Learning Algorithm for Musical Tone Stimulated Wavelet De-Noised EEG Signal Classification
Keywords:Classifier, EEG Signals, Learning Algorithm, Musical Tones,
AbstractThe task of classifying EEG signals pose a challenge in the selection of which learning algorithm is best to provide higher classification accuracy. In this study, five wellknown learning algorithms used in data mining were utilized. The task is to classify musical tone stimulated wavelet de-noised EEG signals. Classification tasks include whether the EEG signal is tone stimulated or not, and whether the EEG signal is stimulated by either the C, F or G tone. Results show higher correct classification instances (CCI) percentages and accuracies in the first classification task using the J48 decision tree as the learning algorithm. For the second classification task, the k-nn learning algorithm outruns the other classifiers but gave low accuracy and low correct classification percentage. The possibility of increasing the performance was explored by increasing the k (number of neighbors). With the increment, its produced directly proportionate in accuracy and correct classification percentage within a certain value of k. A larger k value will reduce the accuracy and the correct classification percentages.
S. Ashok and G. Purushotaman, “DWT based Epileptic Seizure Detection from EEG Signals using Naïve Bayes/k-NN Classifiers,” IEEE Access, vol. 3536, no. c, pp. 1–1, 2016.
S. K. Hadjidimitriou and L. J. Hadjileontiadis, “Toward an EEG-based recognition of music liking using time-frequency analysis,” IEEE Trans. Biomed. Eng., vol. 59, no. 12, pp. 3498–3510, 2012.
M. P. Paulraj, S. Bin Yaccob, A. H. Bin, and K. Subramaniam, “Eeg Based Hearing Threshold Determination Using Artifical Neural Networks,” no. October, pp. 268–270, 2012.
R. F. Navea and E. Dadios, “Classification of tone stimulated EEG signals using independent components and power spectrum vectors,” in 8th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2015, 2016.
R. F. Navea and E. Dadios, “Classification of tone stimulated EEG signals using independent components and power spectrum vectors,” in 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2015, no. December, pp. 1–5.
J. Kim, B. Şen, and et al, “Sleep stage classification based on EEG hilbert-huang transform,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2014, no. 3, pp. 1–6, 2014.
C. Guerrero-Mosquera, M. Verleysen, and A. N. Vazquez, “EEG feature selection using mutual information and support vector machine: A comparative analysis.,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2010, pp. 4946–4949, 2010.
L. Ke and J. Shen, “Classification of EEG signals by ICA and OVRCSP,” Proc. - 2010 3rd Int. Congr. Image Signal Process. CISP 2010, vol. 6, no. d, pp. 2980–2984, 2010.
L. Z. L. Zhang, G. L. G. Liu, and Y. W. Y. Wu, “Wavelet and Common Spatial Pattern for EEG signal feature extraction and classification,” Comput. Mechatronics, Control Electron. Eng. (CMCE), 2010 Int. Conf., vol. 5, no. 4, pp. 243–246, 2010.
M. Casey, J. Thompson, O. Kang, R. Raizada, and T. Wheatley, “Population codes representing musical timbre for high-level fMRI categorization of music genres,” 2012.
S. M. Park and K. B. Sim, “A study on the analysis of auditory cortex active status by music genre: Drawing on EEG,” Proc. - 2011 8th Int. Conf. Fuzzy Syst. Knowl. Discov. FSKD 2011, vol. 3, pp. 1916–1919, 2011.
M. G. Quiñones, A. Kassabian, and E. Boschi, Ubiquitous musics: The everyday sounds that we don’t always notice. Farnham: Ashgate Publishing, Ltd., 2013.
R. F. Navea and E. Dadios, “Classification of Wavelet-denoised Musical Tone Stimulated EEG Signals using Artificial Neural Networks,” in IEEE TENCON, 2016.
R. F. Navea and E. Dadios, “Beta/Alpha power ratio and alpha asymmetry characterization of EEG signals due to musical tone stimulation,” in Project Einstein 2015, 2015.
E. Frank, M. Hall, and I. Witten, “The WEKA Workbench. Online Appendix for ‘Data Mining: Practical Machine Learning Tools and Techniques’, Morgan Kaufmann, 4th Edition.” 2016.
T. Collura, Technical Foundations of Neurofeedback. Abingdon: Routledge, 2014.
E. Estrada, H. Nazeran, G. Sierra, F. Ebrahimi, and S. K. Setarehdan, “Wavelet-based EEG denoising for automatic sleep stage classification,” CONIELECOMP 2011 - 21st Int. Conf. Electron. Commun. Comput. Proc., pp. 295–298, 2011.
N. K. Al-Qazzaz, S. Ali, S. A. Ahmad, M. S. Islam, and M. I. Ariff, “Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task,” IECBES 2014, Conf. Proc. - 2014 IEEE Conf. Biomed. Eng. Sci. “Miri, Where Eng. Med. Biol. Humanit. Meet,” no. December, pp. 214–219, 2015.
R. F. Navea and E. Dadios, “Selection of Mother Wavelet Function and Thresholding Method for Musical Tone Stimulated EEG Signal Denoising and Classification,” in 9th AUN/SEED-Net Conference on Electrical and Electronics Engineering, 2016.
A. Subasi and E. Erçelebi, “Classification of EEG signals using neural network and logistic regression,” Comput. Methods Programs Biomed., vol. 78, no. 2, pp. 87–99, 2005.
V. B. Semwal, M. Raj, and G. C. Nandi, “Biometric gait identification based on a multilayer perceptron,” Robot. Auton. Syst., vol. 65, pp. 65– 75, 2015.
I. Belakhdar, W. Kaaniche, R. Djmel, and B. Ouni, “A Comparison Between ANN and SVM Classifier for Drowsiness Detection Based on Single EEG Channel,” pp. 443–446, 2016.
X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. Mclachlan, A. Ng, B. Liu, P. S. Yu, Z. Z. Michael, S. David, and J. H. Dan, Top 10 algorithms in data mining. 2008.
J. R. Quinlan, “Induction of Decision Trees,” pp. 81–106, 2007.
G. Kaur, “Improved J48 Classification Algorithm for the Prediction of Diabetes,” vol. 98, no. 22, pp. 13–17, 2014.
J. Fleiss, Statistical Methods for Rates and Proportions, 2nd Ed. New York: John Wiley, 1981.
J. Landis and G. Koch, “The measurement of observer agreement for categorical data.,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977.
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