scholarly journals A feature‐based on potential and differential entropy information for electroencephalogram emotion recognition

2021 ◽  
Author(s):  
Dongdong Li ◽  
Li Xie ◽  
Bing Chai ◽  
Zhe Wang
Keyword(s):  
Emotion Recognition ◽  
Differential Entropy ◽  
Entropy Information ◽  
Feature Based

scholarly journals A Multi-Column CNN Model for Emotion Recognition from EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4736 ◽  
Author(s):  
Heekyung Yang ◽  
Jongdae Han ◽  
Kyungha Min
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Deep Neural Network ◽  
Weighted Sum ◽  
Eeg Signals ◽  
Structured Model ◽  
Feature Based ◽  
Improved Accuracy

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.

scholarly journals Physiological Feature Based Emotion Recognition via an Ensemble Deep Autoencoder with Parsimonious Structure

2017 ◽  
Vol 50 (1) ◽  
pp. 6940-6945 ◽  
Author(s):  
Zhong Yin ◽  
Yongxiong Wang ◽  
Wei Zhang ◽  
Li Liu ◽  
Jianhua Zhang ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Physiological Feature ◽  
Feature Based

scholarly journals Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

2020 ◽  
Vol 49 (3) ◽  
pp. 285-298
Author(s):  
Jian Zhang ◽  
Yihou Min
Keyword(s):  
Random Forest ◽  
Emotion Recognition ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Eeg Signal ◽  
Differential Entropy ◽  
Eeg Signals ◽  
Human Emotion ◽  
Before And After

Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of  EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.

scholarly journals Weighted Feature Gaussian Kernel SVM for Emotion Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Wei ◽  
Qingxuan Jia
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Kernel Function ◽  
Recognition Rate ◽  
Gaussian Kernel ◽  
Support Vector ◽  
The Past ◽  
Gaussian Kernel Function ◽  
Feature Based ◽  
Novel Method

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

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