• Title/Summary/Keyword: Human sensibility classifier

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A Study on Application of the Multi-layor Perceptron to the Human Sensibility Classifier with Eletroencephalogram (뇌파의 감성 분류기로서 다층 퍼셉트론의 활용에 관한 연구)

  • Kim, Dong Jun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.11
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    • pp.1506-1511
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    • 2018
  • This study presents a human sensibility evaluation method using neural network and multiple-template method on electroencephalogram(EEG). We used a multi-layer perceptron type neural network as the sensibility classifier using EEG signal. For our research objective, 10-channel EEG signals are collected from the healthy subjects. After the necessary preprocessing is performed on the acquired signals, the various EEG parameters are estimated and their discriminating performance is evaluated in terms of pattern classification capability. In our study, Linear Prediction(LP) coefficients are utilized as the feature parameters extracting the characteristics of EEG signal, and a multi-layer neural network is used for indicating the degree of human sensibility. Also, the estimation for human comfortableness is performed by varying temperature and humidity environment factors and our results showed that the proposed scheme achieved good performances for evaluation of human sensibility.

A Study on the Human Sensibility Evaluation Technique Using EEGs of 4 Emotions (4가지 감정의 뇌파를 이용한 감성평가 기술에 관한 연구)

  • Kim, Dong-Jun;Kang, Dong-Kee;Kim, Heung-Hwan;Yi, Sang-Han;Go, Han-Woo;Park, Se-Jin
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.11
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    • pp.528-534
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    • 2002
  • This paper describes a technique for human sensibility evaluation using EEGs of 4 emotions. The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance suing the proposed method is about 72.6%. This may be promising performance in the human sensibility evaluation.

A Study on the Human Sensibility Evaluation Technique using 10-channel EEG (10채널 뇌파를 이용한 감성평가 기술에 관한 연구)

  • Kim, Heung-Hwan;Lee, Sang-Han;Kang, Dong-Kee;Kim, Dong-Jun;Ko, Han-Woo
    • Proceedings of the KIEE Conference
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    • 2002.07d
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    • pp.2690-2692
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    • 2002
  • This paper describes a technique for human sensibility evaluation using 10-channel EEG(electroencephalogram). The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as, relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance using the proposed method is about 72.6%. This will be promising performance in the human sensibility evaluation.

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A Study on a Human Sensibility Evaluation Technique of EEG using Personality-group Templates (성격 그룹의 템플릿을 이용한 뇌파의 감성평가 기술에 관한 연구)

  • Lee, Sang-Han;Kim, Dong-Jun
    • Proceedings of the KIEE Conference
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    • 2003.07d
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    • pp.2801-2803
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    • 2003
  • This paper describes a technique for human sensibility evaluation using personality-group templates of EEG(electroencephalogram). 10-channel EEGs of 5 extroverts and 5 introverts are collected in comfortable seat, uncomfortable seat and relaxed state. After preprocessing of EEG, the linear predictor coefficients are extracted and used as feature parameters. A neural network based sensibility classifier is designed and the output of the neural network is assumed as the sensibility index. Multiple templates of two personality-groups are stored and the most similar template can be selected by the proposed method. The proposed method showed the better performance than our previous results which have used ungrouped templates.

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Performance Evaluation of Attention-inattetion Classifiers using Non-linear Recurrence Pattern and Spectrum Analysis (비선형 반복 패턴과 스펙트럼 분석을 이용한 집중-비집중 분류기의 성능 평가)

  • Lee, Jee-Eun;Yoo, Sun-Kook;Lee, Byung-Chae
    • Science of Emotion and Sensibility
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    • v.16 no.3
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    • pp.409-416
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    • 2013
  • Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

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Neural-network based Computerized Emotion Analysis using Multiple Biological Signals (다중 생체신호를 이용한 신경망 기반 전산화 감정해석)

  • Lee, Jee-Eun;Kim, Byeong-Nam;Yoo, Sun-Kook
    • Science of Emotion and Sensibility
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    • v.20 no.2
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    • pp.161-170
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    • 2017
  • Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.