A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory

BackgroundEmotion recognition is an increasingly important field of research in brain computer interactions.IntroductionWith the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.MethodsThe current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.ResultsThe presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.ConclusionsThe present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.

[1]  Babak Nadjar Araabi,et al.  A time local subset feature selection for prediction of sudden cardiac death from ECG signal , 2017, Medical & Biological Engineering & Computing.

[2]  U. Rajendra Acharya,et al.  Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals , 2015, Entropy.

[3]  M. Firoozabadi,et al.  COMBINING INDEPENDENT COMPONENT ANALYSIS WITH CHAOTIC QUANTIFIERS FOR THE RECOGNITION OF POSITIVE, NEGATIVE AND NEUTRAL EMOTIONS USING EEG SIGNALS , 2014 .

[4]  Weifeng Liu,et al.  Reinforcement online learning for emotion prediction by using physiological signals , 2017, Pattern Recognit. Lett..

[5]  Singapore,et al.  Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN , 2017 .

[6]  Min,et al.  Nonlinear Analysis of Physiological Time Series , 2007 .

[7]  Yu. Pogoreltsev,et al.  The Application , 2020, How to Succeed in the Academic Clinical Interview.

[8]  C. Grozea,et al.  Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications , 2011, Journal of neural engineering.

[9]  Anisoara Paraschiv-Ionescu,et al.  Nonlinear Analysis of Physiological Time Series , 2009 .

[10]  Zhong Yin,et al.  Recognition of emotions using multimodal physiological signals and an ensemble deep learning model , 2017, Comput. Methods Programs Biomed..

[11]  M. Murugappan,et al.  Human emotion classification using wavelet transform and KNN , 2011, 2011 International Conference on Pattern Analysis and Intelligence Robotics.

[12]  N. Fox,et al.  Asymmetrical brain activity discriminates between positive and negative affective stimuli in human infants. , 1982, Science.

[13]  Ramchandra Manthalkar,et al.  Electroencephalography-Based Emotion Recognition Using Gray-Level Co-occurrence Matrix Features , 2016, CVIP.

[14]  Mohammad Pooyan,et al.  CLASSIFICATION OF BRAIN SIGNALS IN NORMAL SUBJECTS AND PATIENTS WITH EPILEPSY USING MIXTURE OF EXPERTS , 2013 .

[15]  Ahmad Bijar,et al.  A novel approach for detection of deception using Smoothed Pseudo Wigner-Ville Distribution (SPWVD) , 2013 .

[16]  Choong Seon Hong,et al.  Deep Learning based Emotion Recognition through Biosensor Observations , 2016 .

[17]  E. Ebrahimzadeh,et al.  Implementation and Designing of Line-Detection System Based on Electroencephalography (EEG) , 2013 .

[18]  Hugo Vélez-Pérez,et al.  Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling , 2012, Biomed. Signal Process. Control..

[19]  Mohammad Pooyan,et al.  A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals , 2014, PloS one.

[20]  Konstantinos N. Plataniotis,et al.  Affective states classification using EEG and semi-supervised deep learning approaches , 2016, 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP).

[21]  Changde Du,et al.  Semi-supervised Bayesian Deep Multi-modal Emotion Recognition , 2017, ArXiv.

[22]  Mohsen Naji,et al.  Emotion classification during music listening from forehead biosignals , 2015, Signal Image Video Process..

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

[24]  Mangala Gowri,et al.  Energy Density Feature Extraction using Different Wavelets for Emotion Detection , 2018 .

[25]  Mohammad Abdullah-Al-Wadud,et al.  A skin detection approach based on the Dempster-Shafer theory of evidence , 2012, Int. J. Approx. Reason..

[26]  Touradj Ebrahimi,et al.  Implicit emotional tagging of multimedia using EEG signals and brain computer interface , 2009, WSM@MM.

[27]  P. Ekman,et al.  DIFFERENCES Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion , 2004 .

[28]  Gyanendra K. Verma,et al.  Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals , 2014, NeuroImage.

[29]  Chi Zhang,et al.  Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain , 2017, BioMed research international.

[30]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[31]  Elias Ebrahimzadeh,et al.  Linear and nonlinear analyses for detection of sudden cardiac death (SCD) using ECG and HRV signals , 2018 .

[32]  Jyh-Yeong Chang,et al.  Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement , 2011, IEEE Transactions on Biomedical Engineering.

[33]  Li Li,et al.  Emotion recognition based on the sample entropy of EEG. , 2014, Bio-medical materials and engineering.

[34]  Wei Liu,et al.  Emotion Recognition Using Multimodal Deep Learning , 2016, ICONIP.

[35]  Mohammad Mikaeili,et al.  TOWARD A COMPUTER AIDED DIAGNOSIS SYSTEM FOR LUMBAR DISC HERNIATION DISEASE BASED ON MR IMAGES ANALYSIS , 2016 .

[36]  Chun-An Chou,et al.  Recognizing affective state patterns using regularized learning with nonlinear dynamical features of EEG , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[37]  Youjun Li,et al.  Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks , 2017 .

[38]  Elias Ebrahimzadeh,et al.  Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal , 2018, Comput. Methods Programs Biomed..

[39]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[40]  R. Davidson.  Anterior cerebral asymmetry and the nature of emotion , 1992, Brain and Cognition.

[41]  Bao-Liang Lu,et al.  Identifying Stable Patterns over Time for Emotion Recognition from EEG , 2016, IEEE Transactions on Affective Computing.

[42]  Jens Haueisen,et al.  Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity , 2009, Medical & Biological Engineering & Computing.

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

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

[45]  Sima Hoseingholizade,et al.  Studying emotion through nonlinear processing of EEG , 2012 .

[46]  Matthias M. Müller,et al.  Processing of affective pictures modulates right-hemispheric gamma band EEG activity , 1999, Clinical Neurophysiology.

[47]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[48]  Michael D. Robinson,et al.  Measures of emotion: A review , 2009, Cognition & emotion.

[49]  Reza Ebrahimpour,et al.  Combining Neural Networks Based on Dempster-Shafer Theory for Classifying Data with Imperfect Labels , 2010, MICAI.

[50]  Mohammad Pooyan,et al.  ECG SIGNALS NOISE REMOVAL: SELECTION AND OPTIMIZATION OF THE BEST ADAPTIVE FILTERING ALGORITHM BASED ON VARIOUS ALGORITHMS COMPARISON , 2015 .

[51]  Michela Balconi,et al.  Appetitive vs. defensive responses to emotional cues. Autonomic measures and brain oscillation modulation , 2009, Brain Research.

[52]  Moon Inder Singh,et al.  Development of a real time emotion classifier based on evoked EEG , 2017 .

[53]  Mohammad Pooyan,et al.  PREDICTION OF SUDDEN CARDIAC DEATH (SCD) USING TIME-FREQUENCY ANALYSIS OF ECG SIGNALS , 2013 .

[54]  Miyoung Kim,et al.  A Review on the Computational Methods for Emotional State Estimation from the Human EEG , 2013, Comput. Math. Methods Medicine.

[55]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[56]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[57]  Theerasak Chanwimalueang,et al.  Discrimination of emotional states from scalp- and intracranial EEG using multiscale Rényi entropy , 2017, PloS one.

[58]  Majid Nili Ahmadabadi,et al.  Bandit-based local feature subset selection , 2014, Neurocomputing.

[59]  Wei Liu,et al.  Multimodal Emotion Recognition Using Multimodal Deep Learning , 2016, ArXiv.

[60]  M. Just,et al.  Identifying Emotions on the Basis of Neural Activation , 2013, PloS one.

[61]  Mohsen Naji,et al.  Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram , 2014, Cognitive Computation.

[62]  Karim Ansari-Asl,et al.  A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals , 2014, Comput. Methods Programs Biomed..

[63]  Samit Bhattacharya,et al.  Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset , 2017, AAAI.

[64]  Mohammad Pooyan,et al.  Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals , 2011 .

[65]  Elisabeth André,et al.  Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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