scholarly journals LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

2021 ◽  
Author(s):  
Turker Tuncer ◽  
Sengul Dogan ◽  
Abdulhamit Subasi
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Feature Generation ◽  
Eeg Signals ◽  
Recognition Model ◽  
Statistical Feature ◽  
Proposed Model ◽  
Generation Network ◽  
Nonlinear Feature ◽  
Human Brains

AbstractElectroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.

scholarly journals Machine-Learning-Based Emotion Recognition System Using EEG Signals

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 95
Author(s):  
Rania Alhalaseh ◽  
Suzan Alasasfeh
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Emotion Recognition ◽  
Emotional States ◽  
Eeg Signals ◽  
Processing Stage ◽  
Brain Signals ◽  
Specificity And Sensitivity ◽  
Mode Decomposition ◽  
Proposed Model

Many scientific studies have been concerned with building an automatic system to recognize emotions, and building such systems usually relies on brain signals. These studies have shown that brain signals can be used to classify many emotional states. This process is considered difficult, especially since the brain’s signals are not stable. Human emotions are generated as a result of reactions to different emotional states, which affect brain signals. Thus, the performance of emotion recognition systems by brain signals depends on the efficiency of the algorithms used to extract features, the feature selection algorithm, and the classification process. Recently, the study of electroencephalography (EEG) signaling has received much attention due to the availability of several standard databases, especially since brain signal recording devices have become available in the market, including wireless ones, at reasonable prices. This work aims to present an automated model for identifying emotions based on EEG signals. The proposed model focuses on creating an effective method that combines the basic stages of EEG signal handling and feature extraction. Different from previous studies, the main contribution of this work relies in using empirical mode decomposition/intrinsic mode functions (EMD/IMF) and variational mode decomposition (VMD) for signal processing purposes. Despite the fact that EMD/IMFs and VMD methods are widely used in biomedical and disease-related studies, they are not commonly utilized in emotion recognition. In other words, the methods used in the signal processing stage in this work are different from the methods used in literature. After the signal processing stage, namely in the feature extraction stage, two well-known technologies were used: entropy and Higuchi’s fractal dimension (HFD). Finally, in the classification stage, four classification methods were used—naïve Bayes, k-nearest neighbor (k-NN), convolutional neural network (CNN), and decision tree (DT)—for classifying emotional states. To evaluate the performance of our proposed model, experiments were applied to a common database called DEAP based on many evaluation models, including accuracy, specificity, and sensitivity. The experiments showed the efficiency of the proposed method; a 95.20% accuracy was achieved using the CNN-based method.

scholarly journals EEG-Based Estimation on the Reduction of Negative Emotions for Illustrated Surgical Images

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7103
Author(s):  
Heekyung Yang ◽  
Jongdae Han ◽  
Kyungha Min
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Emotional Reaction ◽  
Emotional Reactions ◽  
Fatty Tissue ◽  
Eeg Signals ◽  
Recognition Model ◽  
The Difference ◽  
Electroencephalogram Eeg ◽  
Professional People

Electroencephalogram (EEG) biosignals are widely used to measure human emotional reactions. The recent progress of deep learning-based classification models has improved the accuracy of emotion recognition in EEG signals. We apply a deep learning-based emotion recognition model from EEG biosignals to prove that illustrated surgical images reduce the negative emotional reactions that the photographic surgical images generate. The strong negative emotional reactions caused by surgical images, which show the internal structure of the human body (including blood, flesh, muscle, fatty tissue, and bone) act as an obstacle in explaining the images to patients or communicating with the images with non-professional people. We claim that the negative emotional reactions generated by illustrated surgical images are less severe than those caused by raw surgical images. To demonstrate the difference in emotional reaction, we produce several illustrated surgical images from photographs and measure the emotional reactions they engender using EEG biosignals; a deep learning-based emotion recognition model is applied to extract emotional reactions. Through this experiment, we show that the negative emotional reactions associated with photographic surgical images are much higher than those caused by illustrated versions of identical images. We further execute a self-assessed user survey to prove that the emotions recognized from EEG signals effectively represent user-annotated emotions.

scholarly journals Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Jing Chen ◽  
Haifeng Li ◽  
Lin Ma ◽  
Hongjian Bo ◽  
Frank Soong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Temporal Dynamics ◽  
Optimal Number ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Global Field Power ◽  
Feature Extraction Method ◽  
Microstate Analysis ◽  
Sequence Characteristics

Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

scholarly journals A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021 ◽  
Author(s):  
Huiyun Zhang ◽  
Heming Huang ◽  
Henry Han
Keyword(s):  
Feature Extraction ◽  
Natural Language Processing ◽  
Emotion Recognition ◽  
Language Processing ◽  
Speech Emotion Recognition ◽  
Dense Layer ◽  
Acoustic Model ◽  
Spatiotemporal Information ◽  
Proposed Model ◽  
The Right

Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

scholarly journals A Hybrid Technique using CNN+LSTM for Speech Emotion Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 1126-1130
Keyword(s):  
Feature Extraction ◽  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Hybrid Technique ◽  
Proposed Model ◽  
High Level ◽  
High Level Feature ◽  
Convolutional Lstm

Automatic speech emotion recognition is a very necessary activity for effective human-computer interaction. This paper is motivated by using spectrograms as inputs to the hybrid deep convolutional LSTM for speech emotion recognition. In this study, we trained our proposed model using four convolutional layers for high-level feature extraction from input spectrograms, LSTM layer for accumulating long-term dependencies and finally two dense layers. Experimental results on the SAVEE database shows promising performance. Our proposed model is highly capable as it obtained an accuracy of 94.26%.

scholarly journals A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021 ◽  
Vol 11 (21) ◽  
pp. 9897
Author(s):  
Huiyun Zhang ◽  
Heming Huang ◽  
Henry Han
Keyword(s):  
Feature Extraction ◽  
Natural Language Processing ◽  
Emotion Recognition ◽  
Language Processing ◽  
Speech Emotion Recognition ◽  
Dense Layer ◽  
Acoustic Model ◽  
Spatiotemporal Information ◽  
Proposed Model ◽  
The Right

Speech emotion recognition is a substantial component of natural language processing (NLP). It has strict requirements for the effectiveness of feature extraction and that of the acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address the challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recalls on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

Sign in / Sign up

Export Citation Format

Share Document