EEG Emotion Recognition Based on Sparse Group Lasso-Granger Causality Feature
GUO Jinliang1,2, FANG Fang1, WANG Wei1,2, HE Hanna1,2
1.School of Computer and Information, Hefei University of Technology, Hefei 230601 2.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601
Abstract：Aiming at the current feature extraction based on the functional network level of single brain region, a sparse group lasso-granger causality method is proposed to extract the causal relation among different brain regions as the characteristics of EEG at the effectual brain network level. The α, β and γ EEG bands of participants are extracted. The sparse group lasso algorithm is used to filter the obtained values of the cascade causality measures to acquire high correlation feature subsets as the emotion classification features. Finally the SVM classifier is utilized for emotion classification. Moreover, the ReliefF(filter feature selection) algorithm is employed to select an effective EEG channels to reduce the computational time complexity. The experiments show that the proposed method obtains a higher average emotion classification accuracy on the Valence-Arousal two-dimensional emotion model, and the classification result of the proposed method is better than that of the contrast EEG features. The extracted emotion EEG features can effectively recognize the subjects in different emotional states.
[1] PICARD R W. Affective Computing. Cambridge, USA: The MIT Press, 1997. [2] JADHAV P N, SHANAMUGAN D, CHOURASIA A, et al. Automated Detection and Correction of Eye Blink and Muscular Artefacts in EEG Signal for Analysis of Autism Spectrum Disorder // Proc of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Washington, USA: IEEE, 2014: 1881-1884. [3] ALCHALABI A E, ELSHARNOUBY M, SHIRMOHAMMADI S, et al. Feasibility of Detecting ADHD Patients′ Attention Levels by Classifying Their EEG Signals // Proc of the IEEE International Symposium on Medical Measurements and Applications. Washington, USA: IEEE, 2017: 314-319. [4] NYNI K A, VINCENT L K, VARGHESE L, et al. Wireless Health Monitoring System for ECG, EMG and EEG Detecting // Proc of the 4th International Conference on Innovations in Information, Embe-dded and Communication Systems. Washington, USA: IEEE, 2017. DOI: 10.1109/ICIIECS.2017.8275879. [5] CANDRA H, YUWONO M, CHAI R, et al. EEG Emotion Recognition Using Reduced Channel Wavelet Entropy and Average Wavelet Coefficient Features with Normal Mutual Information Method // Proc of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Washington, USA: IEEE, 2017: 463-466. [6] KUMAR N, KHAUND K, HAZARIKA S M. Bispectral Analysis of EEG for Emotion Recognition. Procedia Computer Science, 2016, 84: 31-35. [7] LIU J X, MENG H Y, NANDI A, et al. Emotion Detection from EEG Recordings // Proc of the 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Washington, USA: IEEE, 2016: 1722-1727. [8] TRIPATHI S, ACHARYA S, SHARMA R D, et al. Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset // Proc of the 29th AAAI Conference on Innovative Applications. Palo Alto, USA: AAAI Press, 2017: 4746-4752. [9] HE Y, AI Q S, CHEN K. A MEMD Method of Human Emotion Recognition Based on Valence-Arousal Model // Proc of the 19th International Conference on Intelligent Human-Machine Systems and Cybernetics. Washington, USA: IEEE, 2017: 399-402. [10] 李 昕,齐晓英,田彦秀,等.基于排列熵与多重分形指数结合的特征提取算法在情感识别中的应用.高技术通讯, 2016, 26(7): 617-624. (LI X, QI X Y, TIAN Y X, et al. Application of the Feature Extraction Algorithm Based on Combination of Permutation Entropy and Multifractal Index in Emotion Recognition. Chinese High Technology Letters, 2016, 26(7): 617-624.) [11] 陆 苗,邹俊忠,张 见,等.基于IMF能量熵的脑电情感特征提取研究.生物医学工程研究, 2016, 35(2): 71-74. (LU M, ZOU J Z, ZHANG J, et al. Emotion Electroencephalograph(EEG) Recognition Based on IMF Energy Entropy. Journal of Biomedical Engineering Research, 2016, 35(2): 71-74.) [12] 聂 聃.基于脑电的情感识别.硕士学位论文.上海:上海交通大学, 2012. (NIE R. Emotion Recognition Based on EEG. Master Dissertation. Shanghai, China: Shanghai Jiaotong University, 2012.) [13] DING M Z, CHEN Y H, BRESSLER S L. Granger Causality Basic Theory and Application to Neuroscience[C/OL]. [2018-03-20]. https://arxiv.org/pdf/q-bio/0608035v1.pdf. [14] ARDOLD A, LIU Y, ABE N. Temporal Causal Modeling with Graphical Granger Methods // Proc of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2007: 66-75. [15] SIMON N, FRIEDMAN J, HASTIE T, et al. A Sparse-Group La-sso. Journal of Computational and Graphical Statistics, 2013, 22(2): 231-245. [16] KOELSTRA S, MUHL C, SOLEYMANI M, et al. DEAP: A Database for Emotion Analysis Using Physiological Signals. IEEE Transactions on Affective Computing, 2012, 3(1): 18-31. [17] DALGLEISH T, POWER M J, EKMAN P, et al. Handbook of Cognition and Emotion. New York, USA: John Wiley & Sons, 2000. [18] RUSSELL J A. A Circumplex Model of Affect. Journal of Persona-lity and Social Psychology, 1980, 39(6): 1161-1178. [19] AYDIN S G, KAYA T, GULER H. Wavelet-Based Study of Valence-Arousal Model of Emotions on EEG Signals with LabVIEW. Brain Informatics, 2016, 3(2): 109-117. [20] 谢 平,杨芳梅,陈晓玲,等.基于多尺度传递熵的脑肌电信号耦合分析.物理学报, 2015, 64(24): 423-432. (XIE P, YANG F M, CHEN X L, et al. Functional Coupling Analyses of Electroencephalogram and Electromyogram Based on Multiscale Transfer Entropy. Acta Physica Sinica, 2015, 64(24): 423-432.) [21] THAMMASAN N, MORIYAMA K, FUKUI K, et al. Familiarity Effects in EEG-Based Emotion Recognition. Brain Informatics, 2016, 4: 39-50. [22] ROBNIK-SˇIKONJA M, KONONENKO I. Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 2003, 53(1/2): 23-69. [23] DE RENZI E, PERANI D, CARLESIMO G A, et al. Prosopagnosia Can Be Associated with Damage Confined to the Right Hemisphere-An MRI and PET Study and a Review of the Literature. Neuropsychologia, 1994, 32(8): 893-902. [24] JATUPAIBOON N, PAN-NGUM S, ISRASENA P. Emotion Cla-ssification Using Minimal EEG Channels and Frequency Bands // Proc of the 10th International Joint Conference on Computer Science and Software Engineering. Washington, USA: IEEE, 2013: 21-24.
2018, Vol. 31 
