Investigation Of Feature Selection Algorithms On A Cognitive Task Classification: A Comparison Study

Year 2018, Volume: 6 Issue: 2, 99 - 104, 30.04.2018

Server Göksel Eraldemir , Mustafa Turan Arslan , Esen Yıldırım

https://doi.org/10.17694/bajece.419549

Cited By: 2

Abstract

In this study, the effects of feature selection on classification of
the electrical signals generated in the brain during numerical and verbal
operations are investigated. 18 healthy university/college students were chosen
for the experimental study. EEG signals were recorded during silent reading and
mental arithmetic operations without using any pen and paper. A total of 60
slides, 30 of which contained reading passages and the rest contained
arithmetic operations, were presented in the experiment.  EEG signals recorded from 26 channels during
the slide show. The recorded EEG signals were analyzed by Hilbert Huang
Transform (HHT), and then features were extracted. 312 features were classified
by Bayesian Network algorithm without applying feature selection with 92.60%
average accuracy. Consistency measures and Correlation based Feature Selection
methods were, then, used for feature selection and the numbers of selected
features are 8 and 39 on average, respectively. Classification accuracies by
using these feature selection algorithms were obtained as 93.98% and 95.58%,
respectively. The results showed that feature selection algorithms contribute
positively to the classification performance.

Keywords

Hilbert Huang Transform, Consistency Measures, Correlation based Feature Selection, EEG Classification

References

• [1]. T. Aflalo. “Decoding motor imagery from the posterior parietal cortex of a tetraplegic human.” Science. vol. 348. no. 6237. pp. 906–910. May 2015. [2]. E. Demirci, “Playing games with brain waves”. TUBITAK Science Technical Journal. 44 (520). 18-24p, 2011 [3]. L. R. Hochberg. “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.” Nature. vol. 485. no. 7398. pp. 372–375. 2012. [4]. F. Cabestaing. T. M. Vaughan. D.J. Mcfarland..J. R. Wolpaw, “Classification of evoked potentials by Pearson ’ s correlation in a Brain-Computer Interface”. Matrix. 67. 156-166pp, 2017. [5]. A. Dogan. M.H. Calp .E.S. ARI. H. Ozkose. “An examination on Brain Computers Interfaces in Human Computer Interaction:Characteristics and Working Principle” [6]. E. Sevinç. “Brain Computer Interfaces”, http://www.rehabilitasyon.com/action/makale/1/Beyin_Bilgisayar_Arayuzleri-2299 (2006) [7]. O. Rioul, and M. Vetterli. “Wavelets and signal processing.” IEEE signal processing magazine 8.4 (1991): 14-38. [8]. N. E. Huang et al.. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. R. Soc. A Math. Phys. Eng. Sci.. vol. 454. no. 1971. pp. 903–995. Mar. 1998. [9]. J. Kortelainen, E. Vayrynen, U. Huuskonen, J. Laurila, J. Koskenkari, J.T. Backman, S. Alahuhta, “Using Hilbert-Huang Transform to assess EEG slow wave activity during anesthesia in post-cardiac arrest patients.”, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando. FL. 2016. pp. 1850-1853. [10]. R. Wang. Y. Wang and C. Luo. “EEG-Based Real-Time Drowsiness Detection Using Hilbert-Huang Transform.” 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou. 2015. pp. 195-198. [11]. K. Rai. V. Bajaj and A. Kumar. “Hilbert-Huang transform based classification of sleep and wake EEG signals using fuzzy c-means algorithm.” International Conference on Communications and Signal Processing (ICCSP). Melmaruvathur. 2015. pp. 0460-0464. [12]. R. Swarnalatha. and D. V. Prasad, “Detection of Sleep Bruxism Based on EEG Hilbert Huang Transform”.5th International Conference on Biomedical Engineering and Technology (ICBET 2015) .IPCBEE vol.81 (2015) (2015) IACSIT Press. Singapore [13]. R. Jenke. A. Peer and M. Buss. “Feature Extraction and Selection for Emotion Recognition from EEG.” in IEEE Transactions on Affective Computing. vol. 5. no. 3. pp. 327-339. July-Sept. 1 2014. [14]. Y. Ji, X. Bu, J. Sun and Z. Liu, “An improved simulated annealing genetic algorithm of EEG feature selection in sleep stage.” 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, 2016, pp. 1-4. [15]. B. Hu, X. Li, S. Sun and M. Ratcliffe, “Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm.” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. PP, no. 99 [16]. C. Cîmpanu. L. Ferariu. T. Dumitriu and F. Ungureanu. “Multi-Objective Optimization of Feature Selection procedure for EEG signals classification.”. 2017 E-Health and Bioengineering Conference (EHB). Sinaia . 2017. pp. 434-437. [17]. A. Onan, S. Korukoglu. “Evalution of feature selection methods in text classification”, Academic Knowledge 2016, Turkey (2016). [18]. S. Zhang1, Z. Zhao. “Feature Selection Filtering Methods for Emotion Recognition in Chinese Speech Signal”. 2008 [19]. L. Jalali. M. Nasiri and B. Minaei. “A hybrid feature selection method based on fuzzy feature selection and consistency measures.” 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. Shanghai. 2009. pp. 718-722. [20]. B. Chakraborty and G. Chakraborty. “Fuzzy Consistency Measure with Particle Swarm Optimization for Feature Selection.” 2013 IEEE International Conference on Systems. Man. and Cybernetics. Manchester. 2013. pp. 4311-4315 [21]. Liu, H., Setiono, R., “A probabilistic approach to feature selection: a filter solution”, Proceedings of the Thirteenth International Conference on Machine Learning, 319-327 (1996). [22]. S. Zhang. and Z. Zhijin, “Feature selection filtering Methods for emotion recognitionin Chinese speech signal.”. Signal Processing. 2008. [23]. T.J. Lee, S.M. Park, K.B. Sim, “Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI”. Journal of Applied Mathematics 2013. pp. 1-9. [24]. S. G. Eraldemir, E. Yildirim, S. Yildirim, Yakup Kutlu, “Feature selection for cognitive EEG signals based channel selection and classification”. Innovations and Applications in Intelligent Systems. 2014 (ASYU 2014). 1-6. [25]. A. Subaşı, M. I. Gursoy, M. “EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications”, 37(12), 8659–8666. https://doi.org/10.1016/j.eswa.2010.06.065, 2010