Evaluating Classifiers for Emotion Recognition Using EEG

There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper, classifying EEG data associated with specific affective/emotional states. The emotions were elicited in the subjects using pictures from the International Affective Picture System (IAPS) database. The raw EEG data were processed to remove artifacts and a number of features were selected as input to the classifiers. The results showed that it is difficult to train a classifier to be accurate over large datasets (15 subjects) but KNN and SVM with the proposed features were reasonably accurate over smaller datasets (5 subjects) identifying the emotional states with an accuracy up to 77.78%.

[1]  W. Pedrycz,et al.  Machine Learning and Cybernetics , 2014, Communications in Computer and Information Science.

[2]  S. Yaacob,et al.  Lifting scheme for human emotion recognition using EEG , 2008, 2008 International Symposium on Information Technology.

[3]  Christine L. Lisetti,et al.  Emotion recognition from physiological signals using wireless sensors for presence technologies , 2004, Cognition, Technology & Work.

[4]  Yuan-Pin Lin,et al.  EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  P. Lang International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[6]  Constantin F. Aliferis,et al.  A Novel Algorithm for Scalable and Accurate Bayesian Network Learning , 2004, MedInfo.

[7]  Xiaodong Li,et al.  AI 2009: Advances in Artificial Intelligence, 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings , 2009, Australasian Conference on Artificial Intelligence.

[8]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[9]  P. Ekman,et al.  Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology. I. , 1990, Journal of personality and social psychology.

[10]  Cristina Conati,et al.  Probabilistic assessment of user's emotions in educational games , 2002, Appl. Artif. Intell..

[11]  Michael Tangermann,et al.  Classification of Artifactual ICA Components , 2009 .

[12]  Glenn F. Wilson,et al.  Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks , 2003, Hum. Factors.

[13]  Léon J. M. Rothkrantz,et al.  Emotion recognition using brain activity , 2008, CompSysTech.

[14]  Nilanjan Sarkar,et al.  Anxiety detecting robotic system – towards implicit human-robot collaboration , 2004, Robotica.

[15]  Minoru Sasaki,et al.  EEG data classification with several mental tasks , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[16]  James C. Christensen,et al.  How Does Day-to-Day Variability in Psychophysiological Data Affect Classifier Accuracy? , 2010 .

[17]  Hamid Parvin,et al.  Validation Based Modified K‐Nearest Neighbor , 2009 .

[18]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[19]  Michael I. Jordan,et al.  A Variational Approach to Bayesian Logistic Regression Models and their Extensions , 1997, AISTATS.

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

[21]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human–robot interaction , 2006, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Omar AlZoubi,et al.  Classification of EEG for Affect Recognition: An Adaptive Approach , 2009, Australasian Conference on Artificial Intelligence.

[23]  Abdul Wahab,et al.  EEG Emotion Recognition System , 2009 .

[24]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[25]  Chin-Teng Lin,et al.  An EEG-based classification system of Passenger's motion sickness level by using feature extraction/selection technologies , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[26]  Hamid Parvin,et al.  MKNN: Modified K-Nearest Neighbor , 2008 .

[27]  Qing Wu,et al.  Classify the number of EEG current sources using support vector machines , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[28]  Leontios J. Hadjileontiadis,et al.  Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis , 2010, IEEE Transactions on Affective Computing.

[29]  Fatma Nasoz,et al.  Emotion Recognition from Physiological Signals for Presence Technologies , 2004 .

[30]  Guo Chen,et al.  A New Machine Double-Layer Learning Method and Its Application in Non-Linear Time Series Forecasting , 2007, 2007 International Conference on Mechatronics and Automation.

[31]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  M. Macas,et al.  Classification of the emotional states based on the EEG signal processing , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[33]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[34]  D. O. Bos,et al.  EEG-based Emotion Recognition The Influence of Visual and Auditory Stimuli , 2007 .

[35]  Rodica Strungaru,et al.  Independent Component Analysis Applied in Biomedical Signal Processing , 2004 .