EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

D. Jude Hemanth

1. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India
*Corresponding Author: D. Jude Hemanth, Email: [email protected]

Identifying human emotions is very important in human machine interaction(HMI). These emotions will affect communication between people, and their mood.Emotion detection will give a clear idea aboutcustomer satisfaction in e-learning, marketing, entertainments and behavior of criminals in law. Artificial neural networksare essential for machine learning and emotion detection.The emotions are detected from EEG signals which can give better performance to audio and facial signals. In this work, several modified Kohonen neural networks are proposed for human emotion classification.EEG signals from DEAP Database are used asinput for ANN to detect the human emotions. Angry, Happy, Sad and Relax are the emotions classified using KohonenNeural Networks.Experimental results show promising results for the proposed approaches.

Kohonen neural network, brain signals, human emotions, classification accuracy

D. Jude Hemanth (2020). EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions. Journal of Artificial Intelligence and Systems, 2, 1–13. https://doi.org/10.33969/AIS.2020.21001.

[1] Yoo, Gilsang, SanghyunSeo, Sungdae Hong, and Hyeoncheol Kim (2016) Emotion extraction based on multi bio-signal using back-propagation neural network. Multimedia Tools and Applications, 1-13.
[2] Yin, Zhong, Mengyuan Zhao, Yongxiong Wang, Jingdong Yang, and Jianhua Zhang (2017) Recognition of emotions using multimodal physiological signals and an ensemble deep learning model. Computer methods and programs in biomedicine, 140 , 93-110.
[3] Yanagimoto, Miku, and Chika Sugimoto (2016) Recognition of persisting emotional valence from EEG using convolutional neural networks. Proceedings of IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA) , 27-32.
[4] Xu, Haiyan, and Konstantinos N. Plataniotis (2016) Affective states classification using EEG and semi-supervised deep learning approaches. Proceedings of IEEE 18th International Workshop on Multimedia Signal Processing (MMSP) 1-6.
[5] Wijeratne, Upani, and UdayangiPerera (2012), Intelligent emotion recognition system using electroencephalography and active shape models. Proceedings of  IEEE EMBS Conference on In Biomedical Engineering and Sciences (IECBES), 636-641.
[6] Vijayan, Aravind E, Deepak Sen, and Sudheer (2015) EEG-based emotion recognition using statistical measures and auto-regressive modeling. Proceedings of IEEE International Conference on Computational Intelligence & Communication Technology (CICT), 587-591.
[7] Ravindran and Malathi.R (2014) Classification of human emotions from EEG signals using filtering and ANFIS classifier. Proceedings of 2nd International Conference on Current Trends in Engineering and Technology (ICCTET), 113-119.
[8] Mohammadpour, Mostafa, Seyyed Mohammad Reza Hashemi, and Negin Houshmand (2017) Classification of EEG-based emotion for BCI applications. Proceedings of IEEE international conference on Artificial Intelligence and Robotics (IRANOPEN), 127-131.
[9] Mohammadi, Zeynab, JavadFrounchi, and Mahmood Amiri (2017) Wavelet-based emotion recognition system using EEG signal. Neural Computing and Applications, 28, no. 8, 1985-1990.
[10] Lekshmi, S. S., Selvam.V., and  Pallikonda Rajasekara (2014) EEG signal classification using principal component analysis and wavelet transform with neural network. Proceedings of IEEE International Conference on Communications and Signal Processing (ICCSP), 687-690.
[11] Ko, Kwang-Eun, Hyun-Chang Yang, and Kwee-Bo Sim (2009) Emotion recognition using EEG signals with relative power values and Bayesian network. International Journal of Control, Automation and Systems, 7, no. 5.
[12] Kaundanya, Vaishnavi. L., Anita Patil, and Ashish Panat (2015) Performance of k-NN classifier for emotion detection using EEG signals. Proceedings of IEEE international conference on Communications and Signal Processing (ICCSP), 1160-1164.
[13] Gao, Yongbin, Hyo Jong Lee, and Raja Majid Mehmood (2015) Deep learning of EEG signals for emotion recognition. Proceedings of IEEE international conference on Multimedia & Expo Workshops  (ICMEW), 1-5.
[14] Bhardwaj, Aayush, Ankit Gupta, Pallav Jain, Asha Rani, and Jyoti Yadav (2015) Classification of human emotions from EEG signals using SVM and LDA Classifiers. Proceedings of 2nd International conference on Signal Processing and Integrated Networks (SPIN), 180-185.
[15] Bajaj, Varun, and Ram Bilas Pachori (2014) Human emotion classification from EEG signals using multiwavelet transform. Proceedings of International Conference on Medical Biometrics, 125-130.
[16] Yang, Yimin, QM Jonathan Wu, Wei-Long Zheng, and Bao-Liang Lu (2017) EEG-based emotion recognition using hierarchical network with subnetwork nodes, IEEE Transactions on Cognitive and Developmental Systems.116-125.
[17] Zhang, Jianhai, Ming Chen, Sanqing Hu, Yu Cao, and Robert Kozma (2016) PNN for EEG-based Emotion Recognition. Proceedings of IEEE international conference on Systems, Man, and Cybernetics (SMC), 002319-002323.
[18] Tripathi, Samarth, Shrinivas Acharya, Ranti Dev Sharma (2017) Sudhanshu Mittal, and Samit Bhattacharya (2017) Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset. Proceedings of International Conference on AAAI, 4746-4752.
[19] Li, Jinpeng, Zhaoxiang Zhang, and Huiguang He (2017) Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition. Cognitive Computation , 1-13.
[20] Thejaswini, S., KM Ravi Kumar, Shyam Rupali, and Vijayendra Abijith (2018) EEG Based Emotion Recognition Using Wavelets and Neural Networks Classifier. Proceedings of International conference on Cognitive Science and Artificial Intelligence (Springer), 101-112.
[21] Mahajan and Rashima (2018) Emotion Recognition via EEG Using Neural Network Classifier. Soft Computing: Theories and Applications - Springer, 429-438.
[22] Tang, Hao, Wei Liu, Wei-Long Zheng, and Bao-Liang Lu (2017) Multimodal Emotion Recognition Using Deep Neural Networks. Proceedings of International Conference on Neural Information Processing (Springer). 811-819.
[23] Lin, Yuan-Pin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, Jeng-Ren Duann, and Jyh-Horng Chen. (2010) EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering , 57, no. 7, 1798-1806.
[24] Anh, Viet Hoang, Manh Ngo Van, Bang Ban Ha, and Thang Huynh Quyet (2012) A real-time model based support vector machine for emotion recognition through EEG. Proceedings of International conference on Control, Automation and Information Sciences (ICCAIS), 191-196.
[25] Pouyanfar, Samira, and Hossein Sameti (2014) Music emotion recognition using two level classification. Proceedings of IEEE international conference on Intelligent Systems (ICIS), 1-6.
[26] S. Koelstra, C. Muehl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras (2012) IEEE Transaction on Affective Computing, vol.3, no.1, pp: 18-31.
[27] D. Jude Hemanth, C Kezi Selva Vijila, A Immanuel Selvakumar and J Anitha (2014) Neurocomputing vol.130, pp: 98-107.
[28] Marcus Santos et al. Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook. Information Fusion, v. 53, p. 222-239, 2020.
[29] Marinho Leandro et al. A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Generation Computer Systems, v. 97, p. 564-577, 2019.
[30] Jardes Rodrigues et al. Classification of EEG Signals to Detect Alcoholism Using Machine Learning Techniques. PATTERN RECOGNITION LETTERS, v. 125, p. 140-149, 2019.
[31] Luis Periera et al. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. NEURAL COMPUTING & APPLICATIONS (INTERNET), v. 31, p. 1317-1329, 2019.
[32] Moraes JL et al. Advances in Photopletysmography Signal Analysis for Biomedical Applications. SENSORS, v. 18, p. 1894-1920, 2018.
[33] Enas Abdulhay et al. Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition. NEURAL COMPUTING & APPLICATIONS, v. 1, p. 1-10, 2018.
[34] Roberto Munoz et al. A new EEG software that supports emotion recognition by using an autonomous approach. NEURAL COMPUTING & APPLICATIONS, v. 1, p. 1-17, 2018.