Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks

Yıl 2017, Cilt: 9 Sayı: 3, 186 - 195, 26.12.2017

Mustafa Turan Arslan Server Göksel Eraldemir Esen Yıldırım

https://doi.org/10.29137/umagd.348871

Cited By: 6

Öz

In
this study, the electrical activities in the brain were classified during
mental mathematical tasks and silent text reading. EEG recordings are collected
from 18 healthy male university/college students, ages ranging from 18 to 25.
During the study, a total of 60 slides including verbal text reading and
arithmetical operations were presented to the subjects. EEG signals were
collected from 26 channels in the course of slide show. Features were extracted
by employing Hilbert Huang Transform (HHT). Then, subject-dependent and
subject-independent classifications were performed using k-Nearest Neighbor (k-NN)
algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications
resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates
were between 92.2% and 97% for subject independent classification. The results
show that EEG data recorded during mathematical and silent reading tasks can be
classified with high accuracy results for both subject-dependent and
subject-independent analysis.

Anahtar Kelimeler

EEG classification, Hilbert Huang Transform, k-Nearest Neighbor

Kaynakça

• Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour, R., & Bagheri, N. (2013). Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Computing and Applications, 23(5), 1319–1327. https://doi.org/10.1007/s00521-012-1074-3
• Bajaj, V., & Pachori, R. B. (2013). Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomedical Engineering Letters, 3(1), 17–21. https://doi.org/10.1007/s13534-013-0084-0
• Ben Dkhil, M., Wali, A., & Alimi, A. M. (2015). Drowsy driver detection by EEG analysis using Fast Fourier Transform. In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 313–318). IEEE. https://doi.org/10.1109/ISDA.2015.7489245
• Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
• Eraldemir, S. G., & Yildirim, E. (2015). Comparison of wavelets for classification of cognitive EEG signals. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 1381–1384). IEEE. https://doi.org/10.1109/SIU.2015.7130099
• Fraiwan, L., Lweesy, K., Khasawneh, N., Fraiwan, M., Wenz, H., & Dickhaus, H. (2011). Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates. Journal of Medical Systems, 35(4), 693–702. https://doi.org/10.1007/s10916-009-9406-2
• Handojoseno, A. M. A., Shine, J. M., Nguyen, T. N., Tran, Y., Lewis, S. J. G., & Nguyen, H. T. (2013). Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson’s Disease patients. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4263–4266). IEEE. https://doi.org/10.1109/EMBC.2013.6610487
• Huang, N. E., Long, S. R., & Shen, Z. (1996). The Mechanism for Frequency Downshift in Nonlinear Wave Evolution. Advances in Applied Mechanics, 32, 59–117C. https://doi.org/10.1016/S0065-2156(08)70076-0
• Jerbic, A. B., Horki, P., Sovilj, S., Isgum, V., & Cifrek, M. (2015). Hilbert-Huang Time-Frequency Analysis of Motor Imagery EEG Data for Brain-Computer Interfaces (pp. 62–65). Springer, Cham. https://doi.org/10.1007/978-3-319-11128-5_16
• Kaplan, A., Fingelkurts, A., Fingelkurts, A., & Borisov, S. (2005). Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges. Signal Processing, 85(11), 2190–2212. Retrieved from http://www.sciencedirect.com/science/article/pii/S0165168405002094
• Kottaimalai, R., Rajasekaran, M. P., Selvam, V., & Kannapiran, B. (2013). EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications. In 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN) (pp. 227–231). IEEE. https://doi.org/10.1109/ICE-CCN.2013.6528498
• Liao, F., Zhang, C., Bian, Z., Xie, D., Kang, M., Li, X., … Yi, M. (2014). Characterizing Heat-Sensitization Responses in Suspended Moxibustion with High-Density EEG. Pain Medicine, 15(8), 1272–1281. https://doi.org/10.1111/pme.12512
• Liao, L.-D., Chen, C.-Y., Wang, I.-J., Chen, S.-F., Li, S.-Y., Chen, B.-W., … Lin, C.-T. (2012). Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. Journal of NeuroEngineering and Rehabilitation, 9(1), 5. https://doi.org/10.1186/1743-0003-9-5
• Lin, C.-F., Su, J.-Y., & Wang, H.-M. (2015). Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism. Journal of Medical Systems, 39(9), 83. https://doi.org/10.1007/s10916-015-0275-6
• Liu, Z.-W., Faraguna, U., Cirelli, C., Tononi, G., & Gao, X.-B. (2010). Direct Evidence for Wake-Related Increases and Sleep-Related Decreases in Synaptic Strength in Rodent Cortex. Journal of Neuroscience, 30(25), 8671–8675. https://doi.org/10.1523/JNEUROSCI.1409-10.2010
• Lo, M.-T., Tsai, P.-H., Lin, P.-F., Lin, C., & Hsin, Y. L. (2009). The Nonlinear and Nonstationary Properties in EEG Signals: Probing The Complex Fluctuations by Hilbert–Huang Transform. Advances in Adaptive Data Analysis, 1(3), 461–482. https://doi.org/10.1142/S1793536909000199
• Lughofer, E., Bouchot, J.-L., & Shaker, A. (2011). On-line elimination of local redundancies in evolving fuzzy systems. Evolving Systems, 2(3), 165–187. https://doi.org/10.1007/s12530-011-9032-3
• Lughofer, E., Smith, J. E., Tahir, M. A., Caleb-Solly, P., Eitzinger, C., Sannen, D., & Nuttin, M. (2009). Human–Machine Interaction Issues in Quality Control Based on Online Image Classification. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39(5), 960–971. https://doi.org/10.1109/TSMCA.2009.2025025
• Meng Hu, Guang Li, Qiuping Ding, & Jiaojie Li. (2005). Classification of Normal and Hypoxia EEG Based on Hilbert Huang Transform. In 2005 International Conference on Neural Networks and Brain (Vol. 2, pp. 851–854). IEEE. https://doi.org/10.1109/ICNNB.2005.1614755
• Ozdemir, N., & Yildirim, E. (2014). Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers. Computational and Mathematical Methods in Medicine, 2014, 572082. https://doi.org/10.1155/2014/572082
• Rahul Kumar Chaurasiya, R. K., Jain, K., Goutam, S., & Manisha. (2015). Epileptic seizure detection using HHT and SVM. In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) (pp. 1–6). IEEE. https://doi.org/10.1109/EESCO.2015.7253660
• Ruan, X., Kun Xue, & Mingai Li. (2014). Feature extraction of SSVEP-based brain-computer interface with ICA and HHT method. In Proceeding of the 11th World Congress on Intelligent Control and Automation (pp. 2418–2423). IEEE. https://doi.org/10.1109/WCICA.2014.7053100
• Samiee, K., Kovacs, P., & Gabbouj, M. (2015). Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform. IEEE Transactions on Biomedical Engineering, 62(2), 541–552. https://doi.org/10.1109/TBME.2014.2360101
• Schalk, G. (2008). Brain–computer symbiosis. Journal of Neural Engineering, 5(1), P1–P15. https://doi.org/10.1088/1741-2560/5/1/P01
• Shih, M.-T., Doctor, F., Fan, S.-Z., Jen, K.-K., & Shieh, J.-S. (2015). Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia. Entropy, 17(3), 928–949. https://doi.org/10.3390/e17030928
• Subasi, A., & Ismail Gursoy, M. (2010). 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
• Vézard, L., Legrand, P., Chavent, M., Faïta-Aïnseba, F., & Trujillo, L. (2015). EEG classification for the detection of mental states. Applied Soft Computing, 32, 113–131. https://doi.org/10.1016/j.asoc.2015.03.028
• Wang, M., Lv, Y., Wen, M., He, S., & Wang, G. (2016). A Fan Control System Base on Steady-State Visual Evoked Potential. In 2016 International Symposium on Computer, Consumer and Control (IS3C) (pp. 81–84). IEEE. https://doi.org/10.1109/IS3C.2016.31
• Wang, R., Wang, Y., & Luo, C. (2015). EEG-Based Real-Time Drowsiness Detection Using Hilbert-Huang Transform. In 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics (pp. 195–198). IEEE. https://doi.org/10.1109/IHMSC.2015.56
• Wolpaw, J. R., Loeb, G. E., Allison, B. Z., Donchin, E., Do Nascimento, O. F., Heetderks, W. J., … Turner, J. N. (2006). BCI Meeting 2005—Workshop on Signals and Recording Methods. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 138–141. https://doi.org/10.1109/TNSRE.2006.875583
• Yang, Z., Yang, L., & Qi, D. (2006). Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform. In Wavelet Analysis and Applications (pp. 543–559). Basel: Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7778-6_40