Abstract
Electroencephalogram (EEG)-based emotion recognition models are gaining interest as they show the intrinsic state of human. A wide range of features are extracted from the scalp EEG recorded using a different set of electrodes across the brain regions. However, there are no standard set of features accepted amongst researchers for emotion recognition. As a result, new researchers in the field use all features reported in the literature which leads to the curse of dimensionality problem and performance degradation due to high correlation within the feature set. Thus, the primary objective of this work is to improve the performance of the emotion recognition model by using an optimal feature set. This research article proposes differential-evolution-based feature selection (DEFS) algorithm to obtain an optimal feature set for effective subject-independent emotion recognition. The optimal feature set obtained from the DEFS algorithm is used to train the SVM classifier. A wide range of experiments are conducted to analyze the performance of our proposed model using a publicly available EEG-based emotion recognition dataset. The proposed model has been compared with several state-of-the-art feature selection and optimization algorithms. The results are analyzed in the aspects of classification performance, fitness value optimization and computational time. In addition, to assure the subject-independent behavior of the proposed model, subject-wise performance has been analyzed. The proposed DEFS-SVM emotion recognition model has got the classification accuracies of 73.60, 74.23, 71.88 and 71.80% to detect valence arousal, valence, dominance, and liking emotional states, respectively. The experimental results assured that our proposed model outperforms all other algorithms in all aspects. Also, the proposed feature selection algorithm is suitable for any EEG-based emotion recognition model to optimize the feature set.
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References
Al-Ani A (2005) Feature subset selection using ant colony optimization. Int J Comput Intell
Al-Qerem A, Kharbat F, Nashwan S, et al (2020) General model for best feature extraction of eeg using discrete wavelet transform wavelet family and differential evolution. Int J Distrib Sens Netw 16(3):1550147720911009
Alarcao SM, Fonseca MJ (2017) Emotions recognition using EEG signals: a survey. IEEE Trans Affect Comput 10(3):374–393
Alazrai R, Alwanni H, Daoud MI (2019) EEG-based BCI system for decoding finger movements within the same hand. Neurosci Lett 698:113–120
Anderson K, McOwan PW (2006) A real-time automated system for the recognition of human facial expressions. IEEE Trans Syst Man Cybern Part B (Cybernetics) 36(1):96–105
Bahassine S, Madani A, Al-Sarem M et al (2020) Feature selection using an improved chi-square for Arabic text classification. J King Saud Univ-Comput Inf Sci 32(2):225–231
Baig MZ, Aslam N, Shum HP et al (2017) Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG. Expert Syst Appl 90:184–195
Cai J, Liu G, Hao M (2009) The research on emotion recognition from ECG signal. In: 2009 International conference on information technology and computer science, IEEE, pp 497–500
Candra H, Yuwono M, Chai R, et al (2015) Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: 37th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 7250–7253
Chen J, Zhang P, Mao Z, et al (2019) Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access 7:44317–44328
Coan JA, Allen JJ (2004) Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol 67(1–2):7–50
Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(02):185–205
Donmez H, Ozkurt N (2019) Emotion classification from EEG signals in convolutional neural networks. In: 2019 Innovations in intelligent systems and applications conference (ASYU), IEEE, pp 1–6
Forgas JP (1995) Mood and judgment: the affect infusion model (aim). Psychol Bull 117(1):39
Gannouni S, Aledaily A, Belwafi K et al (2021) Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification. Sci Rep 11(1):1–17
Gunes C, Ozdemir MA, Akan A (2019) Emotion recognition with multi-channel EEG signals using auditory stimulus. In: 2019 Medical Technologies Congress (TIPTEKNO), pp 1–4. https://doi.org/10.1109/TIPTEKNO.2019.8895124
Gupta R, Falk TH et al (2016) Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174:875–884
Hegazy AE, Makhlouf M, El-Tawel GS (2020) Improved salp swarm algorithm for feature selection. J King Saud Univ-Comput Inf Sci 32(3):335–344
Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29(3):306–310
Hu X, Chu L, Pei J et al (2021) Model complexity of deep learning: a survey. Knowl Inf Syst 63(10):2585–2619
Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Comput 5(3):327–339
Khushaba RN, Al-Ani A, Al-Jumaily A (2011) Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst Appl 38(9):11515–11526
King RB, dela Rosa ED (2019) Are your emotions under your control or not? implicit theories of emotion predict well-being via cognitive reappraisal. Personal Individual Differ 138:177–182
Koelstra S, Muhl C, Soleymani M et al (2011) Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18–31
Kroupi E, Yazdani A, Ebrahimi T (2011) EEG correlates of different emotional states elicited during watching music videos. In: International conference on affective computing and intelligent interaction. Springer, pp 457–466
Laredo D, Ma SF, Leylaz G et al (2020) Automatic model selection for fully connected neural networks. Int J Dyn Control 8(4):1063–1079
Liu J, Wu G, Luo Y et al (2020) EEG-based emotion classification using a deep neural network and sparse autoencoder. Front Syst Neurosci 14:43
Liu W, Zheng WL, Lu BL (2016) Emotion recognition using multimodal deep learning. In: International conference on neural information processing, Springer, pp 521–529
Logesparan L, Rodriguez-Villegas E, Casson AJ (2015) The impact of signal normalization on seizure detection using line length features. Med Biol Eng Comput 53(10):929–942
Makrehchi M, Kamel MS (2005) Text classification using small number of features. In: International workshop on machine learning and data mining in pattern recognition, Springer, pp 580–589
Meier R, Dittrich H, Schulze-Bonhage A et al (2008) Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. J Clin Neurophysiol 25(3):119–131
Mert A, Akan A (2018) Emotion recognition from EEG signals by using multivariate empirical mode decomposition. Pattern Anal Appl 21(1):81–89
Mistry K, Zhang L, Neoh SC et al (2016) A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Trans Cybern 47(6):1496–1509
Mowla MR, Cano RI, Dhuyvetter KJ et al (2020) Affective brain-computer interfaces: choosing a meaningful performance measuring metric. Comput Biol Med 126(104):001
Nakisa B, Rastgoo MN, Tjondronegoro D et al (2018) Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst Appl 93:143–155
Naser DS, Saha G (2013) Recognition of emotions induced by music videos using DT-CWPT. In: 2013 Indian conference on medical informatics and telemedicine (ICMIT), IEEE, pp 53–57
Nie D, Wang XW, Shi LC, et al (2011) EEG-based emotion recognition during watching movies. In: 2011 5th international IEEE/EMBS conference on neural engineering, IEEE, pp 667–670
Panksepp J, Lane RD, Solms M et al (2017) Reconciling cognitive and affective neuroscience perspectives on the brain basis of emotional experience. Neurosci Biobehav Rev 76:187–215
Philippot P, Chapelle G, Blairy S (2002) Respiratory feedback in the generation of emotion. Cognit Emot 16(5):605–627
Picard RW (2000) Affective computing. MIT press
Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer
Putman P, van Peer J, Maimari I et al (2010) EEG theta/beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits. Biol Psychol 83(2):73–78
Raghu S, Sriraam N (2018) Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Syst Appl 113:18–32
Richhariya B, Tanveer M, Rashid A et al (2020) Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomed Signal Process Control 59(101):903
Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1):23–69
Shon D, Im K, Park JH et al (2018) Emotional stress state detection using genetic algorithm-based feature selection on EEG signals. Int J Environ Res Public Health 15(11):2461
Sivagaminathan RK, Ramakrishnan S (2007) A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Syst Appl 33(1):49–60
Song P, Zheng W (2018) Feature selection based transfer subspace learning for speech emotion recognition. IEEE Trans Affect Comput 11(3):373–382
Song T, Zheng W, Lu C, et al (2019) Mped: A multi-modal physiological emotion database for discrete emotion recognition. IEEE Access 7:12177–12191
Sridevi V, Reddy MR, Srinivasan K et al (2019) Improved patient-independent system for detection of electrical onset of seizures. J Clin Neurophysiol 36(1):14
Storn R (1996) On the usage of differential evolution for function optimization. In: Proceedings of North American fuzzy information processing, IEEE, pp 519–523
Subramanian R, Wache J, Abadi MK et al (2018) Ascertain: emotion and personality recognition using commercial sensors. IEEE Trans Affect Comput 9(2):147–160. https://doi.org/10.1109/TAFFC.2016.2625250
Too J, Abdullah AR (2021) A new and fast rival genetic algorithm for feature selection. J Supercomput 77(3):2844–2874
Too J, Abdullah AR (2021) Opposition based competitive grey wolf optimizer for EMG feature selection. Evol Intell 14(4):1691–1705
Too J, Abdullah AR, Mohd Saad N et al (2018) A new competitive binary grey wolf optimizer to solve the feature selection problem in EMG signals classification. Computers 7(4):58
Too J, Abdullah AR, Mohd Saad N et al (2019) EMG feature selection and classification using a Pbest-guide binary particle swarm optimization. Computation 7(1):12
Too J, Mafarja M, Mirjalili S (2021) Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach. Neural Comput Appl 33(23):16229–16250
Van Der Vinne N, Vollebregt MA, Van Putten MJ, et al (2017) Frontal alpha asymmetry as a diagnostic marker in depression: fact or fiction? a meta-analysis. Neuroimage: Clinical 16:79–87
Wen T, Zhang Z (2017) Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification. Medicine 96(19)
Wolpaw JR, Birbaumer N, Heetderks WJ et al (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–173
Wu G, Liu G, Hao M (2010) The analysis of emotion recognition from GSR based on PSO. In: 2010 International symposium on intelligence information processing and trusted computing, IEEE, pp 360–363
Yin Z, Liu L, Chen J et al (2020) Locally robust EEG feature selection for individual-independent emotion recognition. Expert Syst Appl 162(113):768
Zhang S, Zhao Z (2008) Feature selection filtering methods for emotion recognition in Chinese speech signal. In: 2008 9th international conference on signal processing, IEEE, pp 1699–1702
Zhang T, Zheng W, Cui Z et al (2016) A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans Multimedia 18(12):2528–2536
Zhang Y, Chen J, Tan JH et al (2020) An investigation of deep learning models for EEG-based emotion recognition. Front Neurosci 14(622):759
Zheng W (2016) Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis. IEEE Trans Cognit Dev Syst 9(3):281–290
Zheng W, Zhou X, Zou C et al (2006) Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Trans Neural Netw 17(1):233-238
Acknowledgements
K. Kannadasan acknowledges Ministry of Human Resource Development (MHRD)—India to support a research grant through the Prime Minister’s Research Fellows (PMRF) Scheme—December 2020 cycle.
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Kannadasan, K., Veerasingam, S., Shameedha Begum, B. et al. An EEG-based subject-independent emotion recognition model using a differential-evolution-based feature selection algorithm. Knowl Inf Syst 65, 341–377 (2023). https://doi.org/10.1007/s10115-022-01762-w
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DOI: https://doi.org/10.1007/s10115-022-01762-w