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

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.

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

Brain Sciences ◽

10.3390/brainsci9110326 ◽

2019 ◽

Vol 9 (11) ◽

pp. 326 ◽

Cited By ~ 5

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.

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Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01810-5 ◽

2020 ◽

Vol 2020 (1) ◽

Cited By ~ 1

Author(s):

Shaoqiang Wang ◽

Shudong Wang ◽

Song Zhang ◽

Yifan Wang

Keyword(s):

Deep Learning ◽

Network Structure ◽

Signal Recognition ◽

Eeg Signals ◽

Real World Data ◽

Data Set ◽

Practical Applications ◽

Epilepsy Diagnosis ◽

Single Data ◽

Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

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Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

10.21203/rs.3.rs-866042/v1 ◽

2021 ◽

Author(s):

Naveen Kumari ◽

Rekha Bhatia

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Deep Convolutional Neural Network ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Learning Models ◽

Recognition Model ◽

Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Complexity ◽

10.1155/2020/6816502 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Hao Chao ◽

Liang Dong ◽

Yongli Liu ◽

Baoyun Lu

Keyword(s):

Deep Learning ◽

Emotion Recognition ◽

Affective Computing ◽

Learning Model ◽

Support Vector ◽

Eeg Signals ◽

Global Synchronization ◽

Spatial Characteristics ◽

Maximal Information Coefficient ◽

Deep Learning Model

Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization information across all channels. This paper proposes a global feature extraction method that encapsulates the multichannel EEG signals into gray images. The maximal information coefficient (MIC) for all channels was first measured. Subsequently, an MIC matrix was constructed according to the electrode arrangement rules and represented by an MIC gray image. Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. Experiments were conducted on the benchmark dataset for emotion analysis using EEG, physiological, and video signals. The experimental results demonstrated that the global synchronization features and spatial characteristics are beneficial for recognizing emotions and the proposed deep learning model effectively mines and utilizes the two salient features.

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Criminal psychological emotion recognition based on deep learning and EEG signals

Neural Computing and Applications ◽

10.1007/s00521-020-05024-0 ◽

2020 ◽

Cited By ~ 1

Author(s):

Qi Liu ◽

Hongguang Liu

Keyword(s):

Deep Learning ◽

Emotion Recognition ◽

Eeg Signals

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Matching pursuit algorithm for enhancing EEG signal quality and increasing the accuracy and efficiency of emotion recognition

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0327 ◽

2020 ◽

Vol 65 (4) ◽

pp. 393-404

Author(s):

Ali Momennezhad

Keyword(s):

Emotion Recognition ◽

Matching Pursuit ◽

Recognition Algorithm ◽

Eeg Signals ◽

User Friendliness ◽

Signal Collection ◽

Average Accuracy ◽

Matching Pursuit Algorithm ◽

User Friendly ◽

Electroencephalogram Eeg

AbstractIn this paper, we suggest an efficient, accurate and user-friendly brain-computer interface (BCI) system for recognizing and distinguishing different emotion states. For this, we used a multimodal dataset entitled “MAHOB-HCI” which can be freely reached through an email request. This research is based on electroencephalogram (EEG) signals carrying emotions and excludes other physiological features, as it finds EEG signals more reliable to extract deep and true emotions compared to other physiological features. EEG signals comprise low information and signal-to-noise ratios (SNRs); so it is a huge challenge for proposing a robust and dependable emotion recognition algorithm. For this, we utilized a new method, based on the matching pursuit (MP) algorithm, to resolve this imperfection. We applied the MP algorithm for increasing the quality and SNRs of the original signals. In order to have a signal of high quality, we created a new dictionary including 5-scale Gabor atoms with 5000 atoms. For feature extraction, we used a 9-scale wavelet algorithm. A 32-electrode configuration was used for signal collection, but we used just eight electrodes out of that; therefore, our method is highly user-friendly and convenient for users. In order to evaluate the results, we compared our algorithm with other similar works. In average accuracy, the suggested algorithm is superior to the same algorithm without applying MP by 2.8% and in terms of f-score by 0.03. In comparison with corresponding works, the accuracy and f-score of the proposed algorithm are better by 10.15% and 0.1, respectively. So as it is seen, our method has improved past works in terms of accuracy, f-score and user-friendliness despite using just eight electrodes.

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Video‐triggered EEG‐emotion public databases and current methods: A survey

Brain Science Advances ◽

10.26599/bsa.2020.9050026 ◽

2020 ◽

Vol 6 (3) ◽

pp. 255-287

Author(s):

Wanrou Hu ◽

Gan Huang ◽

Linling Li ◽

Li Zhang ◽

Zhiguo Zhang ◽

...

Keyword(s):

Emotion Recognition ◽

External Environment ◽

Research Direction ◽

High Time Resolution ◽

Work Efficiency ◽

Eeg Signals ◽

Laboratory Environment ◽

Modeling Methods ◽

Rich Media ◽

Electroencephalogram Eeg

Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.

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Time-ResNeXt for epilepsy recognition based on EEG signals

10.1101/2019.12.27.889238 ◽

2019 ◽

Author(s):

shaoqiang Wang ◽

Yifan Wang ◽

Shudong Wang

Keyword(s):

Deep Learning ◽

Network Structure ◽

Signal Recognition ◽

Eeg Signals ◽

Real World Data ◽

Data Set ◽

Practical Applications ◽

Epilepsy Diagnosis ◽

Single Data ◽

Electroencephalogram Eeg

AbstractObjectiveTo automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust.MethodBased on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals.ResultsThe accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 90.50%.ConclusionThe Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset), and has great potential for improving clinical practice.

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GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

10.21203/rs.3.rs-1085276/v1 ◽

2021 ◽

Author(s):

Jian Zhao ◽

ZhiWei Zhang ◽

Jinping Qiu ◽

Lijuan Shi ◽

Zhejun KUANG ◽

...

Keyword(s):

Deep Learning ◽

Emotion Recognition ◽

Frequency Domain ◽

Rapid Development ◽

Semantic Features ◽

Hemispheric Differences ◽

Recognition Model ◽

Model Based ◽

Spatial Features ◽

Convolution Kernels

Abstract With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

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