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

Although detecting emotional states from brain dynamics is an issue 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 to determine emotional states using (positive-neutral-negative) Electroencephalogram (EEG) signals. By using this method, a hybrid structure is created to obtain features from EEG signals. In the study, Seed EEG dataset including 15 positive subjects and positive-neutral-negative emotional state is used. In proposed method, a classification task is utilized with the basis of individuals by using 27 EEG channels from left hemisphere of each subject. Level 5 is separated by applying EMD to EEG segments that contain three emotional states. Features are obtained from the Intrinsic Mode Functions (IMFs) 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 is 83.77% with k-NN classifier and 84.50% with the ANN classifier. Furthermore, the highest classification performance is found to be 96.75% with the k-NN classifier. The results obtained in the study support related studies in the literature.

PDF

___

[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.