EEG-based Emotion Classification using Deep Belief Networks · Deep Belief Networks (DBN) is a...
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EEG-based Emotion Classification using Deep Belief Networks Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Among the approaches to emotion recognition, methods based on electroencephalogram (EEG) sig- nals are more reliable because of its high accuracy and objective evaluation compared to other external appearance such as facial expression and gesture. However due to the low signal to noise ratio (SNR), it is often very hard to analyze EEG signals ‘by hand’ even for neurophysiologists. Recent developing deep learning in machine learning community allows au- tomated feature extraction and feature selection and eliminates the limitation of hand-crafted feature. gamma frequency bands do exist. Second, we show that differential entropy (DE) features extracted from EEG data possess accurate and stable information for emotion classification. Finally, the paper com- pares the results between deep modals and shallow models like KNN, SVM and GELM. Moreover, DBN-HMM performs well when compared with the state-of-the-art classification methods. Deep Belief Networks (DBN) is a probabilistic gen- erative model with deep architecture, which charac- terizes the input data distribution using hidden vari- ables. A DBN is constructed by stacking a predefined number of restricted Boltzmann machines (RBMs) on top of each other where the output from a lower-lev- el RBM is the input to a higher-level RBM. The graphical depiction of DBN Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, and Bao-Liang Lu Feature Extraction Raw Data SVM Feature Extraction Raw Data DBN HMM Feature Extraction Raw Data DBN (b) (d) (e) Feature Extraction Raw Data GELM (c) Feature Extraction Raw Data KNN (a) Overview of the five setups for EEG-based emotion classification used in this work Session K-2 Session K -1 Session K Session K+1 Session K+2 Hint of start Movie clip Rest 10 sec 4 min 20 sec Posi ve Emoon Posi ve Emoon Posi ve Emoon Negave Emoon Negave Emoon Protocol of the EEG experiment ... ... ... 500 1000 1500 2000 2500 50 100 150 200 250 300 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Time Delta Theta Alpha Beta Gamma Frequency bands There exist specific neural patterns in beta and gamma bands for positive and negative emotions Delta Theta Alpha Beta Gamma Total 0 10 20 30 40 50 60 70 80 90 100 Frequency Band s Accuracy (%) GELM SVM DBN DBN-HMM Introduction Method Conclusion In this paper, we introduce recent advanced deep learning models to EEG-based emotion classifica- tion. The main contributions of this paper are as fol- lows: First, we find that neural signatures associated with positive and negative emotions in beta and IEEE International Conference on Multimedia & Expo (ICME), 2014
Transcript of EEG-based Emotion Classification using Deep Belief Networks · Deep Belief Networks (DBN) is a...
EEG-based Emotion Classificationusing Deep Belief Networks
Key Lab. of Shanghai Education Commission forIntelligent Interaction and Cognitive Engineering
Among the approaches to emotion recognition, methods based on electroencephalogram (EEG) sig-nals are more reliable because of its high accuracy and objective evaluation compared to other external appearance such as facial expression and gesture. However due to the low signal to noise ratio (SNR), it is often very hard to analyze EEG signals ‘by hand’even for neurophysiologists. Recent developing deep learning in machine learning community allows au-tomated feature extraction and feature selection and eliminates the limitation of hand-crafted feature.
gamma frequency bands do exist. Second, we show that differential entropy (DE) features extracted from EEG data possess accurate and stable information for emotion classification. Finally, the paper com-pares the results between deep modals and shallow models like KNN, SVM and GELM. Moreover, DBN-HMM performs well when compared with the state-of-the-art classification methods.
Deep Belief Networks (DBN) is a probabilistic gen-erative model with deep architecture, which charac-terizes the input data distribution using hidden vari-ables. A DBN is constructed by stacking a predefined number of restricted Boltzmann machines (RBMs) on top of each other where the output from a lower-lev-el RBM is the input to a higher-level RBM.
The graphical depiction of DBN
Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, and Bao-Liang Lu
FeatureExtraction
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SVM
FeatureExtraction
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FeatureExtraction
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KNN
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Overview of the five setups for EEG-based emotion classification used in this work
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Protocol of the EEG experiment
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Conclusion In this paper, we introduce recent advanced deep learning models to EEG-based emotion classifica-tion. The main contributions of this paper are as fol-lows: First, we find that neural signatures associated with positive and negative emotions in beta and
IEEE International Conference on Multimedia & Expo (ICME), 2014
