EEG based emotion monitoring using wavelet and learning vector quantization

Emotional identification is necessary for example in Brain Computer Interface (BCI) application and when emotional therapy and medical rehabilitation take place. Some emotional states can be characterized in the frequency of EEG signal, such excited, relax and sad. The signal extracted in certain frequency useful to distinguish the three emotional state. The classification of the EEG signal in real time depends on extraction methods to increase class distinction, and identification methods with fast computing. This paper proposed human emotion monitoring in real time using Wavelet and Learning Vector Quantization (LVQ). The process was done before the machine learning using training data from the 10 subjects, 10 trial, 3 classes and 16 segments (equal to 480 sets of data). Each data set processed in 10 seconds and extracted into Alpha, Beta, and Theta waves using Wavelet. Then they become input for the identification system using LVQ three emotional state that is excited, relax, and sad. The results showed that by using wavelet we can improve the accuracy of 72% to 87% and number of training data variation increased the accuracy. The system was integrated with wireless EEG to monitor emotion state in real time with change each 10 seconds. It takes 0.44 second, was not significant toward 10 seconds.

[1]  M. Murugappan,et al.  Time-Frequency Analysis of EEG Signals for Human Emotion Detection , 2008 .

[2]  K. Hong,et al.  CLASSIFYING MENTAL ACTIVITIES FROM EEG-P 300 SIGNALS USING ADAPTIVE NEURAL NETWORKS , 2012 .

[3]  Suprijanto,et al.  Identification of alertness state based on EEG signal using wavelet extraction and neural networks , 2014, 2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA).

[4]  Yasue Mitsukura,et al.  Extraction of unconscious emotions while watching TV commercials , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

[5]  Esmeralda C. Djamal,et al.  EEG-based recognition of attention state using wavelet and support vector machine , 2016, 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA).

[6]  Olga Sourina,et al.  Real-Time EEG-Based Emotion Recognition and Its Applications , 2011, Trans. Comput. Sci..

[7]  Bao-Liang Lu,et al.  EEG-based emotion recognition during watching movies , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[8]  Sayan Nag,et al.  Emotion specification from musical stimuli: An EEG study with AFA and DFA , 2017, 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN).

[9]  A. B. M. Aowlad Hossain,et al.  Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network , 2015 .

[10]  A. L. Leiman,et al.  State-dependent choice behavior in the Rhesus monkey. , 1971, Neuropsychologia.

[11]  R. Nagarajan,et al.  Comparison of different wavelet features from EEG signals for classifying human emotions , 2009, 2009 IEEE Symposium on Industrial Electronics & Applications.

[12]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[13]  Thierry Pun,et al.  Multimodal Emotion Recognition in Response to Videos , 2012, IEEE Transactions on Affective Computing.

[14]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level by using wavelet transform and artificial neural network , 2004, Journal of Neuroscience Methods.

[15]  Ram Bilas Pachori,et al.  Human Emotion Classification from EEG Signals Using Multiwavelet Transform , 2014, 2014 International Conference on Medical Biometrics.

[16]  D. Pélisson,et al.  Automatic online control of motor adjustments in reaching and grasping , 2014, Neuropsychologia.

[17]  Hyouk Ryeol Choi,et al.  Pattern Recognition of Human Grasping Operations Based on EEG , 2006 .

[18]  Yaacob Sazali,et al.  Classification of human emotion from EEG using discrete wavelet transform , 2010 .

[19]  Bao-Liang Lu,et al.  EEG-Based Emotion Recognition in Listening Music by Using Support Vector Machine and Linear Dynamic System , 2012, ICONIP.

[20]  Abdul Wahab,et al.  EEG Emotion Recognition Based on the Dimensional Models of Emotions , 2013 .

[21]  A. Wróbel,et al.  EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. , 2013, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[22]  Yuan-Pin Lin,et al.  Support vector machine for EEG signal classification during listening to emotional music , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[23]  Dimitrios Tzovaras,et al.  Automatic Recognition of Boredom in Video Games Using Novel Biosignal Moment-Based Features , 2011, IEEE Transactions on Affective Computing.

[24]  S. Murugappan,et al.  Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT) , 2013, 2013 IEEE 9th International Colloquium on Signal Processing and its Applications.

[25]  Mauridhi Hery Purnomo,et al.  Classification of human state emotion from physiological signal pattern using pulse sensor based on learning vector quantization , 2016, 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA).

[26]  Olga Sourina,et al.  Real-Time EEG-Based Human Emotion Recognition and Visualization , 2010, 2010 International Conference on Cyberworlds.

[27]  A. Zabidi,et al.  Classification of EEG signal from imagined writing using a combined Autoregressive model and multi-layer perceptron , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[28]  John Atkinson,et al.  Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers , 2016, Expert Syst. Appl..

[29]  Jyh-Horng Chen,et al.  Multilayer perceptron for EEG signal classification during listening to emotional music , 2007, TENCON 2007 - 2007 IEEE Region 10 Conference.

[30]  Mohiuddin Ahmad,et al.  Human emotion recognition using frequency & statistical measures of EEG signal , 2013, 2013 International Conference on Informatics, Electronics and Vision (ICIEV).

[31]  Andrea Petracca,et al.  A real-time classification algorithm for EEG-based BCI driven by self-induced emotions , 2015, Comput. Methods Programs Biomed..

[32]  Ning-Han Liu,et al.  Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors , 2013, Sensors.

[33]  Nitin Kumar,et al.  Bispectral Analysis of EEG for Emotion Recognition , 2015, IHCI.

[34]  Suhua Zhang,et al.  An approach to EEG-based emotion recognition using combined feature extraction method , 2016, Neuroscience Letters.

[35]  Asha Rani,et al.  Classification of human emotions from EEG signals using SVM and LDA Classifiers , 2015, 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).

[36]  Esmeralda C. Djamal,et al.  CLASSIFICATION OF EEG-BASED HAND GRASPING IMAGINATION USING AUTOREGRESSIVE AND NEURAL NETWORKS , 2015 .

[37]  Z. Mahmoodin,et al.  Selection of Symlets wavelet function order for EEG signal feature extraction in children with dyslexia , 2015, 2015 IEEE Student Symposium in Biomedical Engineering & Sciences (ISSBES).

[38]  Esmeralda C. Djamal,et al.  Brain Computer Interface Game Controlling Using Fast Fourier Transform and Learning Vector Quantization , 2017 .

[39]  W. Mansor,et al.  An analysis of EEG signal generated from grasping and writing , 2011, 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE).

[40]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.