Research Article
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Year 2020, , 313 - 321, 30.12.2020
https://doi.org/10.36222/ejt.807971

Abstract

References

  • [1] Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of neuroscience methods, 123(1), 69-87.
  • [2] Sharma, R., & Pachori, R. B. (2015). Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications, 42(3), 1106-1117.
  • [3] Acharya, U. R., Sree, S. V., Swapna, G., Martis, R. J., & Suri, J. S. (2013). Automated EEG analysis of epilepsy: a review. Knowledge-Based Systems, 45, 147-165.
  • [4] Kumar, T. S., Kanhangad, V., & Pachori, R. B. (2015). Classification of seizure and seizure-free EEG signals using local binary patterns. Biomedical Signal Processing and Control, 15, 33-40.
  • [5] Kaya, Y., Uyar, M., Tekin, R., & Yıldırım, S. (2014). 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Applied Mathematics and Computation, 243, 209-219.
  • [6] Mert, A., & Akan, A. (2018). Emotion recognition from EEG signals by using multivariate empirical mode decomposition. Pattern Analysis and Applications, 21(1), 81-89.
  • [7] Gupta, V., Chopda, M. D., & Pachori, R. B. (2018). Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals. IEEE Sensors Journal, 19(6), 2266-2274.
  • [8] Seed Dataset. available online: http://bcmi.sjtu.edu.cn/~seed/
  • [9] Rato, R. T., Ortigueira, M. D., & Batista, A. G. (2008). On the HHT, its problems, and some solutions. Mechanical systems and signal processing, 22(6), 1374-1394.
  • [10] Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), 2037-2041.
  • [11] Chatlani, N., & Soraghan, J. J. (2010, August). Local binary patterns for 1-D signal processing. In 2010 18th European Signal Processing Conference (pp. 95-99). IEEE.
  • [12] Kuang, Q., & Zhao, L. (2009). A practical GPU based kNN algorithm. In Proceedings. The 2009 International Symposium on Computer Science and Computational Technology (ISCSCI 2009) (p. 151). Academy Publisher.
  • [13] Li, X., Song, D., Zhang, P., Zhang, Y., Hou, Y., & Hu, B. (2018). Exploring EEG features in cross-subject emotion recognition. Frontiers in neuroscience, 12, 162.
  • [14] Cho, J., & Hwang, H. (2020). Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network. Sensors, 20(12), 3491.
  • [15] Qing, C., Qiao, R., Xu, X., & Cheng, Y. (2019). Interpretable emotion recognition using EEG signals. IEEE Access, 7, 94160-94170.

DETERMINATION OF EMOTIONAL STATUS FROM EEG TIME SERIES BY USING EMD BASED LOCAL BINARY PATTERN METHOD

Year 2020, , 313 - 321, 30.12.2020
https://doi.org/10.36222/ejt.807971

Abstract

Although determining emotional states from brain dynamics has been a subject that has been studied for a long time, the desired level has not been reached yet. In this study, Empirical mode decomposition (EMD) based Local Binary Pattern (LBP) method is proposed for emotional determination using (positive-neutral-negative) Electroencephalogram (EEG) signals. Thanks to this method, a hybrid structure was created in obtaining features from EEG signals. In the study, Seed EEG dataset containing 15 positive subjects and positive-neutral-negative emotional state is used. In the study, classification is utilized with the basis of individuals by using 27 EEG channels in the left hemisphere of each subject. Level 5 was separated by applying EMD to EEG segments containing three emotional states. Features were obtained from the Intrinsic mode function (IMF) using LBP method. These features are classified with k Nearest Neighbor (k-NN) and Artificial Neural Network (ANN). The average classification accuracy for 15 participants was 83.77% using the k-NN classifier and 84.50% with the ANN classifier. In addition, the highest classification performance was found to be 96.75% with the k-NN classifier. The results obtained in the study support similar studies in the literature.

References

  • [1] Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of neuroscience methods, 123(1), 69-87.
  • [2] Sharma, R., & Pachori, R. B. (2015). Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications, 42(3), 1106-1117.
  • [3] Acharya, U. R., Sree, S. V., Swapna, G., Martis, R. J., & Suri, J. S. (2013). Automated EEG analysis of epilepsy: a review. Knowledge-Based Systems, 45, 147-165.
  • [4] Kumar, T. S., Kanhangad, V., & Pachori, R. B. (2015). Classification of seizure and seizure-free EEG signals using local binary patterns. Biomedical Signal Processing and Control, 15, 33-40.
  • [5] Kaya, Y., Uyar, M., Tekin, R., & Yıldırım, S. (2014). 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Applied Mathematics and Computation, 243, 209-219.
  • [6] Mert, A., & Akan, A. (2018). Emotion recognition from EEG signals by using multivariate empirical mode decomposition. Pattern Analysis and Applications, 21(1), 81-89.
  • [7] Gupta, V., Chopda, M. D., & Pachori, R. B. (2018). Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals. IEEE Sensors Journal, 19(6), 2266-2274.
  • [8] Seed Dataset. available online: http://bcmi.sjtu.edu.cn/~seed/
  • [9] Rato, R. T., Ortigueira, M. D., & Batista, A. G. (2008). On the HHT, its problems, and some solutions. Mechanical systems and signal processing, 22(6), 1374-1394.
  • [10] Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), 2037-2041.
  • [11] Chatlani, N., & Soraghan, J. J. (2010, August). Local binary patterns for 1-D signal processing. In 2010 18th European Signal Processing Conference (pp. 95-99). IEEE.
  • [12] Kuang, Q., & Zhao, L. (2009). A practical GPU based kNN algorithm. In Proceedings. The 2009 International Symposium on Computer Science and Computational Technology (ISCSCI 2009) (p. 151). Academy Publisher.
  • [13] Li, X., Song, D., Zhang, P., Zhang, Y., Hou, Y., & Hu, B. (2018). Exploring EEG features in cross-subject emotion recognition. Frontiers in neuroscience, 12, 162.
  • [14] Cho, J., & Hwang, H. (2020). Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network. Sensors, 20(12), 3491.
  • [15] Qing, C., Qiao, R., Xu, X., & Cheng, Y. (2019). Interpretable emotion recognition using EEG signals. IEEE Access, 7, 94160-94170.
There are 15 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Ömer Türk 0000-0002-0060-1880

Publication Date December 30, 2020
Published in Issue Year 2020

Cite

APA Türk, Ö. (2020). DETERMINATION OF EMOTIONAL STATUS FROM EEG TIME SERIES BY USING EMD BASED LOCAL BINARY PATTERN METHOD. European Journal of Technique (EJT), 10(2), 313-321. https://doi.org/10.36222/ejt.807971

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