scholarly journals Positive and Negative Emotion Classification Based on Multi-channel

2021 ◽  
Vol 15 ◽  
Author(s):  
Fangfang Long ◽  
Shanguang Zhao ◽  
Xin Wei ◽  
Siew-Cheok Ng ◽  
Xiaoli Ni ◽  
...  
Keyword(s):  
Frequency Band ◽  
Classification Accuracy ◽  
Negative Emotion ◽  
Negative Emotions ◽  
Energy Ratio ◽  
Svm Classifier ◽  
Differential Entropy ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Eeg Features

The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.

scholarly journals Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

2022 ◽  
Vol 19 (2) ◽  
pp. 629
Author(s):  
Yuting Wang ◽  
Shujian Wang ◽  
Ming Xu
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Classification Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Landscape Perception ◽  
Landscape Types ◽  
Electroencephalogram Eeg ◽  
Eeg Features

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

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 Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective

2021 ◽  
Vol 11 (11) ◽  
pp. 1424
Author(s):  
Yuhong Zhang ◽  
Yuan Liao ◽  
Yudi Zhang ◽  
Liya Huang
Keyword(s):  
Classification Accuracy ◽  
Active Regions ◽  
Classification Performance ◽  
Sensor Data ◽  
Single Trial ◽  
Eeg Signals ◽  
Emergency Braking ◽  
Node Importance ◽  
Novel Approach ◽  
Eeg Features

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm–Feature selection–Classification System (KFCS)is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.

SVM CLASSIFIER FOR EPILEPTIC SEIZURE PREDICTION USING SUB-BANDING OF IEEG SIGNALS

2021 ◽  
pp. 2150024
Author(s):  
C. Sudalaimani ◽  
N. Sivakumaran ◽  
P. Devanand ◽  
G. Alexander ◽  
S. Rominus Valsalam
Keyword(s):  
Frequency Band ◽  
Epileptic Seizure ◽  
Noise Removal ◽  
Support Vector ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Seizure Prediction ◽  
Pass Filter ◽  
Number Of Patients ◽  
Eeg Features

Epileptic seizure in the brain affects the day-to-day life of any individual due to its unexpected nature of occurrence. It has affected more than 50 million people worldwide. Drug resistance of patients is an important factor which leads to failure of epilepsy treatments using medications in 30% of patients. Surgery is also not a viable option in a substantial number of patients. In such cases, a new kind of seizure forecasting system is necessary to help those people. In our work, various sub-frequency bands of EEG signals are produced from the originally recorded Intracranial Electroencephalogram (IEEG) signals of five canines and two persons to identify possible low complex and less intense EEG features from each sub-band of the entire spectrum. Support Vector Machine (SVM) with different Kernel-based classifiers are used to categorize features into preictal and interictal data. Epileptic Seizures forecasting accuracy of 99% has been achieved for data from canine and human. Employed wavelet filter for noise removal and found that it improved the seizure prediction accuracy in some subjects and reduced the accuracy in some subjects. Similarly, the feature selection technique also improved the preictal detection accuracy in some patients/subjects and reduced the accuracy in some data. From this work, we identified that seizure prediction is possible in at least one sub-frequency band especially in high gamma sub-band generated from the originally recorded signal using a high-pass filter. This work demonstrates an algorithm for seizure forecasting or identifying the preictal region which identifies the suitable best sub-frequency band for predicting the seizure of the originally recorded EEG data by using the computationally less intense EEG features and employing the best classifying SVM kernel.

Epileptic seizure detection from EEG signals with phase–amplitude cross-frequency coupling and support vector machine

2018 ◽  
Vol 32 (08) ◽  
pp. 1850086 ◽  
Author(s):  
Yang Liu ◽  
Jiang Wang ◽  
Lihui Cai ◽  
Yingyuan Chen ◽  
Yingmei Qin
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Epileptic Seizures ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Frequency Coupling ◽  
Phase Amplitude ◽  
Cross Frequency Coupling

As a pattern of cross-frequency coupling (CFC), phase–amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB–MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6[Formula: see text]s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

Transcending Time and Space with Digital Emotional Support

2020 ◽  
Author(s):  
Tyler Colasante ◽  
Lauren Lin ◽  
Kalee DeFrance ◽  
Tom Hollenstein
Keyword(s):  
Emotional Support ◽  
Negative Emotion ◽  
Negative Emotions ◽  
Good News ◽  
Social Avoidance ◽  
Time And Space ◽  
Beneficial Effects ◽  
Digital Devices ◽  
Wide Range ◽  
Socially Isolated

In the current digital age, emotional support is increasingly received through digital devices. However, virtually all studies assessing the benefits of emotional support have focused on in-person support. Using an experience sampling methodology, we assessed participants’ negative emotions, digital and in-person support for those emotions, and success in regulating them three times per day for 14 days, thus covering a wide range of digital support scenarios (N = 164 participants with 6,530 collective measurement occasions). We also considered whether participants were alone versus with others at the time of their negative emotion and higher versus lower in social avoidance as plausible moderators of when digital support was utilized and effective. We expected more pronounced use and efficacy of digital support when participants were alone and higher in trait social avoidance. However, digital support was used and perceived as effective for regulating negative emotions regardless of these factors and its beneficial effects were on par with those of traditional in-person support. The unique benefits of digital support may not be restricted to socially isolated or socially avoidant users. These findings are timely given the widespread anxiety and isolation under the current COVID-19 pandemic. If transcending time and space with digital emotional support is the new norm, the good news is that it seems to be working.

Age Advantages of Emotional Experience during the COVID-19 Pandemic are Robust, but Diminished Compared to Pre-pandemic Conditions

2020 ◽  
Author(s):  
Rui Sun ◽  
Disa Sauter
Keyword(s):  
Older Adults ◽  
Large Scale ◽  
Negative Emotion ◽  
Emotional Experience ◽  
Negative Emotions ◽  
Older Individuals ◽  
Selection Strategies ◽  
Scale Test ◽  
Prolonged Stress

Getting old is generally seen as unappealing, yet aging confers considerable advantages in several psychological domains (North & Fiske, 2015). In particular, older adults are better off emotionally than younger adults, with aging associated with the so-called “age advantages,” that is, more positive and less negative emotional experiences (Carstensen et al., 2011). Although the age advantages are well established, it is less clear whether they occur under conditions of prolonged stress. In a recent study, Carstensen et al (2020) demonstrated that the age advantages persist during the COVID-19 pandemic, suggesting that older adults are able to utilise cognitive and behavioural strategies to ameliorate even sustained stress. Here, we build on Carstensen and colleagues’ work with two studies. In Study 1, we provide a large-scale test of the robustness of Carstensen and colleagues’ finding that older individuals experience more positive and less negative emotions during the COVID-19 pandemic. We measured positive and negative emotions along with age information in 23,629 participants in 63 countries in April-May 2020. In Study 2, we provide a comparison of the age advantages using representative samples collected before and during the COVID-19 pandemic. We demonstrate that older people experience less negative emotion than younger people during the prolonged stress of the COVID-19 pandemic. However, the advantage of older adults was diminished during the pandemic, pointing to a likely role of older adults use of situation selection strategies (Charles, 2010).

scholarly journals Anthropomorphic Strategies Promote Wildlife Conservation through Empathy: The Moderation Role of the Public Epidemic Situation

2021 ◽  
Vol 18 (7) ◽  
pp. 3565
Author(s):  
Dan Yue ◽  
Zepeng Tong ◽  
Jianchi Tian ◽  
Yang Li ◽  
Linxiu Zhang ◽  
...  
Keyword(s):  
College Students ◽  
Behavioral Intention ◽  
Wildlife Conservation ◽  
Negative Emotion ◽  
Public Awareness ◽  
Negative Emotions ◽  
Experimental Studies ◽  
Wildlife Trade ◽  
Illegal Wildlife Trade ◽  
The Impact

The global illegal wildlife trade directly threatens biodiversity and leads to disease outbreaks and epidemics. In order to avoid the loss of endangered species and ensure public health security, it is necessary to intervene in illegal wildlife trade and promote public awareness of the need for wildlife conservation. Anthropomorphism is a basic and common psychological process in humans that plays a crucial role in determining how a person interacts with other non-human agents. Previous research indicates that anthropomorphizing nature entities through metaphors could increase individual behavioral intention of wildlife conservation. However, relatively little is known about the mechanism by which anthropomorphism influences behavioral intention and whether social context affects the effect of anthropomorphism. This research investigated the impact of negative emotions associated with a pandemic situation on the effectiveness of anthropomorphic strategies for wildlife conservation across two experimental studies. Experiment 1 recruited 245 college students online and asked them to read a combination of texts and pictures as anthropomorphic materials. The results indicated that anthropomorphic materials could increase participants’ empathy and decrease their wildlife product consumption intention. Experiment 2 recruited 140 college students online and they were required to read the same materials as experiment 1 after watching a video related to epidemics. The results showed that the effect of wildlife anthropomorphization vanished if participants’ negative emotion was aroused by the video. The present research provides experimental evidence that anthropomorphic strategies would be useful for boosting public support for wildlife conservation. However, policymakers and conservation organizations must be careful about the negative effects of the pandemic context, as the negative emotions produced by it seems to weaken the effectiveness of anthropomorphic strategies.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

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