Using GA-based feature selection for emotion recognition from physiological signals

Gu, Yanfeng; Tan, Soon Keat; Wong, Kai-Juan; Ho, Min-Huang; Qu, Li

doi:10.1109/ispacs.2009.4806747

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…Feature reduction could help to increase the speed, performance and stability of the computation [18] [34]. In order to select the relevant features for efficient emotion classification, various feature reduction algorithms such as Genetic Algorithm (GA) [26] [20], Min-Redundancy-Max-Relevance (MRMR) [15] [36], ReliefF and Effect-Size (ES)-Based Feature Selection etc., has been used by various researchers. GA-based feature selection method is quite reliable and powerful which results in finding the best feature subset for this particular application.…”

Section: Feature Reductionmentioning

confidence: 99%

“…The electrical potential is nothing but the voltage between pairs of these electrodes which are then filtered, amplified, and recorded. Brain waves have been classified into four different frequency bands [21]: delta (0-4 Hz), theta (4-8 Hz), alpha (812 Hz) and beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30).…”

Section: Emotion Recognition Based On Eegmentioning

confidence: 99%

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Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

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Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

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Section: Feature Reductionmentioning

confidence: 99%

Section: Emotion Recognition Based On Eegmentioning

confidence: 99%

Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

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“…The emotion recognition system used five types of biosensors to attach on the subjects in order to make the experiment [6]. The IAPS was employed to elicit emotion for subjects and feature extraction utilized six statistical features of physiological signals.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers have used the different classification technique by trying the best as possible in order to solve the emotional recognition problem and improve the accuracy result. four types of classification methods were employed to classify emotions after comparing the accuracy of these methods such as kNN (k-Nearest Neighbor), fuzzy-kNN, discriminant function analysis with linear (LDF), quadratic (QDF) kernels, and support vector machine (SVM) and so on [6]. Especially, SVMs are widely used for many purpose in various field [12].…”

Section: Related Workmentioning

confidence: 99%

Physiological Responses-Based Emotion Recognition Using Multi-Class SVM with RBF Kernel

Vanny¹,

Ko²,

Park³

et al. 2013

Journal of Institute of Control, Robotics and Systems

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Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

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“…• Speech emotion recognition approaches in human computer interaction ( GU et al, 2009;HAAG et al, 2004;MERA;ICHIMURA, 2004…”

Section: Trabalhos Relacionadosmentioning

confidence: 99%

Uma abordagem para indicar o estado emocional de usuários em tempo de interação

Gonçalves¹

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A Deus, pela sua presença constante em minha vida. Obrigado Jesus! A minha família e parentes pelo amor, carinho e apoio incondicionais. Em especial, aos meus pais Lucicléa e Clovis, a minha irmã Lucélia e minha sobrinha Juju que acabou de chegar ao mundo. Amo muito vocês! Ao Prof. Jó Ueyama, por sua orientação presente e pelo perfil multidisciplinar. À Prof. a Vânia Neris, por sua orientação presente e pelo comprometimento. Ao Jó e a Vânia pela amizade e por tudo mais. À Prof. a Thienne Johnson, por sua orientação e apoio no Doutorado Sanduíche. Ao Gabriel Gian, Sibelius e Gabriel Martins meus amigos e alunos de IC, pelo apoio técnico e risadas nos momentos tensos. Ao Eduardo, Victor, Alexandrina, Caio e Alan pela revisão do meu texto e apoio nas questões da psicologia e IA. Ao Hospital Espírita de Marília pela parceria no Projeto de Extensão. Aos amigos do LaSDPC e LIFeS, por compartilharem conhecimento e vivências. As Reps Tacacrazy e Uva Preta que fizeram a minha vida em São Carlos mais divertida. Aos amigos do caaso e federupas, paraenses e agregados, por participarem dos meus experimentos. Aos amigos da 3Atos e Fábrica de Artes, por terem incluído as artes cênicas na minha vida. Aos amigos da IEQ-Botafogo que me acolheram como um filho em São Carlos. Aos membros da Banca Examinadora, pelas contribuições no trabalho. Aos Profissionais da USP, pela postura Ética. À CAPES, CNPQ, FAPESP, ICMC/USP e UofA, pelo apoio financeiro. "As emoções são intermináveis. Quanto mais as exprimimos, mais maneiras temos de as exprimir."

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Using GA-based feature selection for emotion recognition from physiological signals

Cited by 9 publications

References 12 publications

Emotion Recognition Based on Various Physiological Signals - A Review

Emotion Recognition Based on Various Physiological Signals - A Review

Physiological Responses-Based Emotion Recognition Using Multi-Class SVM with RBF Kernel

Uma abordagem para indicar o estado emocional de usuários em tempo de interação

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