EEG-based emotion recognition during watching movies

Nie, Dan; Wang, Xiaowei; Shi, Li-Chen; Lu, Bao-Liang

doi:10.1109/ner.2011.5910636

Cited by 294 publications

(134 citation statements)

References 7 publications

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“…They achieved a recognition rate of 41.7% for five emotion states. In our previous work, we proposed an emotion recognition system using the power spectrum of EEG as features [11]. Our experimental results indicated that the recognition rate using a support vector machine reached an accuracy of 87.5% for two emotion states .…”

Section: Introductionmentioning

confidence: 90%

EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines

Wang

Nie

2011

Neural Information Processing

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180

107

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Abstract. Information about the emotional state of users has become more and more important in human-machine interaction and braincomputer interface. This paper introduces an emotion recognition system based on electroencephalogram (EEG) signals. Experiments using movie elicitation are designed for acquiring subject's EEG signals to classify four emotion states, joy, relax, sad, and fear. After pre-processing the EEG signals, we investigate various kinds of EEG features to build an emotion recognition system. To evaluate classification performance, knearest neighbor (kNN) algorithm, multilayer perceptron and support vector machines are used as classifiers. Further, a minimum redundancymaximum relevance method is used for extracting common critical features across subjects. Experimental results indicate that an average test accuracy of 66.51% for classifying four emotion states can be obtained by using frequency domain features and support vector machines.

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Section: Introductionmentioning

confidence: 90%

EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines

Wang

Nie

2011

Neural Information Processing

Self Cite

180

107

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“…By using good and strong stimulation, emotion recognition is more likely to be performed with better results and higher accuracy. There are some types of stimulation as follow: pictures, 12-43 video clips, [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] music, [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76] memories, 77 self-induction, 78,80,81 environment elicitation like light, humidity and temperature, 79 games, 82 etc. Some ways of eliciting emotions and some induced emotions are listed in Table 1.…”

Section: Emotion Stimulationmentioning

confidence: 99%

A Review on EEG Signals Based Emotion Recognition

Soroush

Maghooli

Setarehdan

et al. 2017

Int Clin Neurosci J

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“…Pioneer of them can be given as Ajit Abraham [13] Jang et al [14], Mendel [15] and Wu & Mendal [16], Emilio SoriaOlivas et al [17], Ching Long Su et al [18], John & Coupland [19], Oscar Castillo & Patricia Melin [20]. Much application specific work is done by various researchers including Malkawi & Murad [21], Nie et al [22], Bouzaidaa et al [23] and Azriyenni & Mustafa [24].…”

Section: Hybrid Systemsmentioning

confidence: 99%

Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy Systems

Sajja¹

2017

IJEACS

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Abstract-Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.

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EEG-based emotion recognition during watching movies

Cited by 294 publications

References 7 publications

EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines

EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines

A Review on EEG Signals Based Emotion Recognition

Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy Systems

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