Emotion recognition from EEG signals by using multivariate empirical mode decomposition

This paper explores the advanced properties of empirical mode decomposition (EMD) and its multivariate extension (MEMD) for emotion recognition. Since emotion recognition using EEG is a challenging study due to nonstationary behavior of the signals caused by complicated neuronal activity in the brain, sophisticated signal processing methods are required to extract the hidden patterns in the EEG. In addition, multichannel analysis is another issue to be considered when dealing with EEG signals. EMD is a recently proposed iterative method to analyze nonlinear and nonstationary time series. It decomposes a signal into a set of oscillations called intrinsic mode functions (IMFs) without requiring a set of basis functions. In this study, a MEMD-based feature extraction method is proposed to process multichannel EEG signals for emotion recognition. The multichannel IMFs extracted by MEMD are analyzed using various time and frequency domain techniques such as power ratio, power spectral density, entropy, Hjorth parameters and correlation as features of valance and arousal scales of the participants. The proposed method is applied to the DEAP emotional EEG data set, and the results are compared with similar previous studies for benchmarking.

[1]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  J. Russell.  A circumplex model of affect. , 1980 .

[3]  Paulo Gonçalves,et al.  Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[4]  Danilo P. Mandic,et al.  Filter Bank Property of Multivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Signal Processing.

[5]  Mohammed Imamul Hassan Bhuiyan,et al.  Detection of Seizure and Epilepsy Using Higher Order Statistics in the EMD Domain , 2013, IEEE Journal of Biomedical and Health Informatics.

[6]  B. Hjorth.  EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[7]  Po-Lei Lee,et al.  Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing , 2011, Journal of Neuroscience Methods.

[8]  Aydin Akan,et al.  Detrended fluctuation thresholding for empirical mode decomposition based denoising , 2014, Digit. Signal Process..

[9]  Jon D. Morris.  Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1 , 1995 .

[10]  Dilbag Singh,et al.  Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine , 2014, Comput. Electr. Eng..

[11]  Benxiong Huang,et al.  An alternative envelope approach for empirical mode decomposition , 2010, Digit. Signal Process..

[12]  Keikichi Hirose,et al.  Voiced/non-voiced speech classification using adaptive thresholding with bivariate EMD , 2016, Pattern Analysis and Applications.

[13]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[14]  Aydin Akan,et al.  Breast Cancer Detection with Reduced Feature Set , 2015, Comput. Math. Methods Medicine.

[15]  Ram Bilas Pachori,et al.  Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition , 2011, Comput. Methods Programs Biomed..

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

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

[18]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[19]  Bao-Liang Lu,et al.  Emotional state classification from EEG data using machine learning approach , 2014, Neurocomputing.

[20]  Yanli Yang,et al.  An Analytical Expression for Empirical Mode Decomposition Based on B-Spline Interpolation , 2013, Circuits Syst. Signal Process..

[21]  Christofer Toumazou,et al.  Empirical Mode Decomposition: Real-Time Implementation and Applications , 2013, J. Signal Process. Syst..

[22]  Angelo Perkusich,et al.  Emotion Recognition Based on Brain-Computer Interface Systems , 2013 .

[23]  Goutam Saha,et al.  Classification of emotions induced by music videos and correlation with participants' rating , 2014, Expert Syst. Appl..

[24]  Gabriel Rilling,et al.  One or Two Frequencies? The Empirical Mode Decomposition Answers , 2008, IEEE Transactions on Signal Processing.

[25]  Pasin Israsena,et al.  EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation , 2014, TheScientificWorldJournal.

[26]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

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

[28]  Yuan-Pin Lin,et al.  EEG-Based Emotion Recognition in Music Listening , 2010, IEEE Transactions on Biomedical Engineering.

[29]  Danilo P. Mandic,et al.  Empirical Mode Decomposition for Trivariate Signals , 2010, IEEE Transactions on Signal Processing.