Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, Cilt: 10 Sayı: 2, 313 - 321, 30.12.2020

Ömer Türk

https://doi.org/10.36222/ejt.807971
Cited By: 1

Öz

Kaynakça

  • [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

Yıl 2020, Cilt: 10 Sayı: 2, 313 - 321, 30.12.2020

Ömer Türk

https://doi.org/10.36222/ejt.807971
Cited By: 1

Öz

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.

Anahtar Kelimeler

EEG Emotion EMD LBP k-NN ANN

Kaynakça

  • [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.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Araştırma Makalesi
Yazarlar

Ömer Türk 0000-0002-0060-1880

Yayımlanma Tarihi 30 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 10 Sayı: 2

Kaynak Göster

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

Cited By

The convolutional neural network approach from electroencephalogram signals in emotional detection
Concurrency and Computation: Practice and Experience
https://doi.org/10.1002/cpe.6356