2017 International Joint Conference on Neural Networks (IJCNN) 2017
DOI: 10.1109/ijcnn.2017.7966409
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Emotional state estimation using a modified gradient-based neural architecture with weighted estimates

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Cited by 8 publications
(3 citation statements)
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“…We used a non-parametric DE estimator by Kozachenko and Leonenko [ 66 ] that estimates the differential entropy of a continuous random variable using nearest neighbour distance [ 67 ]. It is worthy of note that a number of previous studies has adapted DE for emotion classification [ 68 , 69 ].…”
Section: Methodsmentioning
confidence: 99%
“…We used a non-parametric DE estimator by Kozachenko and Leonenko [ 66 ] that estimates the differential entropy of a continuous random variable using nearest neighbour distance [ 67 ]. It is worthy of note that a number of previous studies has adapted DE for emotion classification [ 68 , 69 ].…”
Section: Methodsmentioning
confidence: 99%
“…This might be because there is the significant difference of static EEG signals among different time periods, which has a great interference to classification. [20], support vector machine using DE features (SVM) [20], minimalist neural network (MNN) [26], space-temporal recurrent neural network (STRNN) [14], dynamical graph convolutional neural networks (DGCNN) [18], Bimodal deep autoencoder (BDAE) [22], Graph regularized extreme learning machine (GELM) [27], and SyncNet [28]. We apply several representative methods on our dataset for comparison, the methods and models involved are as follows: PSD+SVM: Classical emotion recognition method on EEG signals, where PSD features are extracted from each channel of EEG signals at five specific frequency bands (delta: 1-3 Hz, theta: 4-7 Hz, alpha: 8-13 Hz, beta: 14-30 Hz, gamma: 31-50 Hz), and are fed into the traditional SVM.…”
Section: A Overall Performancementioning
confidence: 99%
“…To deliver effective services, some companies employ speech emotion recognition technology to track customers' feelings [2]. Other researchers in the human-agent interaction field developed emotion detection systems to improve user experiences [3].…”
Section: Introductionmentioning
confidence: 99%