scholarly journals Emotion Variation from Controlling Contrast of Visual Contents through EEG-Based Deep Emotion Recognition

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4543 ◽  
Author(s):  
Heekyung Yang ◽  
Jongdae Han ◽  
Kyungha Min
Keyword(s):  
Emotion Recognition ◽  
Eeg Signals ◽  
Human Emotions ◽  
Human Participants ◽  
Valence And Arousal ◽  
And Control

Visual contents such as movies and animation evoke various human emotions. We examine an argument that the emotion from the visual contents may vary according to the contrast control of the scenes contained in the contents. We sample three emotions including positive, neutral and negative to prove our argument. We also sample several scenes of these emotions from visual contents and control the contrast of the scenes. We manipulate the contrast of the scenes and measure the change of valence and arousal from human participants who watch the contents using a deep emotion recognition module based on electroencephalography (EEG) signals. As a result, we conclude that the enhancement of contrast induces the increase of valence, while the reduction of contrast induces the decrease. Meanwhile, the contrast control affects arousal on a very minute scale.

EEG-Based Music Mood Analysis and Applications

2013 ◽  
Vol 712-715 ◽  
pp. 2726-2730
Author(s):  
Xin Xu ◽  
Hui Guan ◽  
Zhen Liu ◽  
Bo Jun Wang
Keyword(s):  
Emotion Recognition ◽  
Adjuvant Treatment ◽  
Eeg Signals ◽  
Eeg Activity ◽  
Different Types ◽  
Human Emotions

Music is known to be a powerful elicitor of emotions. Music with different moods induces various emotions, each of which corresponding to certain pattern of EEG signals. In this paper, based on current music mood categories, we discuss how the music belonging to different mood types affect the pattern EEG activity. We review several literatures verifying that certain characteristics of EEG differ from each other induced by different types of music. Such differences make it possible for emotion recognition through EEG signals. We also introduce some applications of emotional music such as improvement of human emotions and adjuvant treatment of diseases.

scholarly journals Emotion recognition based on multi-channel EEG signals

2021 ◽  
Vol 2078 (1) ◽  
pp. 012028
Author(s):  
Huiping Shi ◽  
Hong Xie ◽  
Mengran Wu
Keyword(s):  
Emotion Recognition ◽  
Attention Mechanism ◽  
Electrode Position ◽  
Two Dimensional ◽  
Eeg Signals ◽  
Recognition Method ◽  
Emotional Interaction ◽  
Spatial Features ◽  
Valence And Arousal ◽  
Gated Recurrent Unit

Abstract Emotion recognition is a key technology of human-computer emotional interaction, which plays an important role in various fields and has attracted the attention of many researchers. However, the issue of interactivity and correlation between multi-channel EEG signals has not attracted much attention. For this reason, an EEG signal emotion recognition method based on 2DCNN-BiGRU and attention mechanism is tentatively proposed. This method firstly forms a two-dimensional matrix according to the electrode position, and then takes the pre-processed two-dimensional feature matrix as input, in the two-dimensional convolutional neural network (2DCNN) and the bidirectional gated recurrent unit (BiGRU) with the attention mechanism layer Extract spatial features and time domain features in, and finally classify by softmax function. The experimental results show that the average classification accuracy of this model are 93.66% and 94.32% in the valence and arousal, respectively.

scholarly journals EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1870
Author(s):  
Tianjiao Kong ◽  
Jie Shao ◽  
Jiuyuan Hu ◽  
Xin Yang ◽  
Shiyiling Yang ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Complex Network ◽  
Research Area ◽  
Angle Measurement ◽  
Eeg Signals ◽  
Horizontal Visibility ◽  
Visibility Graphs ◽  
Single Feature ◽  
Active Research ◽  
Valence And Arousal

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

scholarly journals Personality first in emotion: a deep neural network based on electroencephalogram channel attention for cross-subject emotion recognition

2021 ◽  
Vol 8 (8) ◽  
pp. 201976
Author(s):  
Zhihang Tian ◽  
Dongmin Huang ◽  
Sijin Zhou ◽  
Zhidan Zhao ◽  
Dazhi Jiang
Keyword(s):  
Emotion Recognition ◽  
Recognition Accuracy ◽  
Personality Factors ◽  
Eeg Signals ◽  
Recognition Experiment ◽  
Feature Information ◽  
Micro Level ◽  
Spatio Temporal ◽  
Valence And Arousal ◽  
Electroencephalogram Eeg

In recent years, more and more researchers have focused on emotion recognition methods based on electroencephalogram (EEG) signals. However, most studies only consider the spatio-temporal characteristics of EEG and the modelling based on this feature, without considering personality factors, let alone studying the potential correlation between different subjects. Considering the particularity of emotions, different individuals may have different subjective responses to the same physical stimulus. Therefore, emotion recognition methods based on EEG signals should tend to be personalized. This paper models the personalized EEG emotion recognition from the macro and micro levels. At the macro level, we use personality characteristics to classify the individuals’ personalities from the perspective of ‘birds of a feather flock together’. At the micro level, we employ deep learning models to extract the spatio-temporal feature information of EEG. To evaluate the effectiveness of our method, we conduct an EEG emotion recognition experiment on the ASCERTAIN dataset. Our experimental results demonstrate that the recognition accuracy of our proposed method is 72.4% and 75.9% on valence and arousal, respectively, which is 10.2% and 9.1% higher than that of no consideration of personalization.

scholarly journals Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition

2021 ◽  
Vol 14 ◽  
Author(s):  
Yinfeng Fang ◽  
Haiyang Yang ◽  
Xuguang Zhang ◽  
Han Liu ◽  
Bo Tao
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Rapid Development ◽  
Support Vector ◽  
Emotion Classification ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Power Spectral ◽  
Deep Forest ◽  
Human Emotions

Due to the rapid development of human–computer interaction, affective computing has attracted more and more attention in recent years. In emotion recognition, Electroencephalogram (EEG) signals are easier to be recorded than other physiological experiments and are not easily camouflaged. Because of the high dimensional nature of EEG data and the diversity of human emotions, it is difficult to extract effective EEG features and recognize the emotion patterns. This paper proposes a multi-feature deep forest (MFDF) model to identify human emotions. The EEG signals are firstly divided into several EEG frequency bands and then extract the power spectral density (PSD) and differential entropy (DE) from each frequency band and the original signal as features. A five-class emotion model is used to mark five emotions, including neutral, angry, sad, happy, and pleasant. With either original features or dimension reduced features as input, the deep forest is constructed to classify the five emotions. These experiments are conducted on a public dataset for emotion analysis using physiological signals (DEAP). The experimental results are compared with traditional classifiers, including K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). The MFDF achieves the average recognition accuracy of 71.05%, which is 3.40%, 8.54%, and 19.53% higher than RF, KNN, and SVM, respectively. Besides, the accuracies with the input of features after dimension reduction and raw EEG signal are only 51.30 and 26.71%, respectively. The result of this study shows that the method can effectively contribute to EEG-based emotion classification tasks.

scholarly journals Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

2021 ◽  
Vol 15 ◽  
Author(s):  
Yanling An ◽  
Shaohai Hu ◽  
Xiaoying Duan ◽  
Ling Zhao ◽  
Caiyun Xie ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Spatial Information ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Eeg Signals ◽  
Key Technologies ◽  
Convolutional Autoencoder ◽  
Valence And Arousal ◽  
Electroencephalogram Eeg

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5135
Author(s):  
Ngoc-Dau Mai ◽  
Boon-Giin Lee ◽  
Wan-Young Chung
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Temporal Lobe ◽  
Affective Computing ◽  
Electrode Placement ◽  
Entropy Measure ◽  
Support Vector ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Computing Method

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

scholarly journals A EEG-based emotion recognition model with rhythm and time characteristics

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jianzhuo Yan ◽  
Shangbin Chen ◽  
Sinuo Deng
Keyword(s):  
Emotion Recognition ◽  
Time Scales ◽  
Time Scale ◽  
Short Term Memory ◽  
Recognition Model ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Valence And Arousal ◽  
Memory Characteristics

Abstract As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.

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