Emotion Recognition From EEG Signals of Hearing-Impaired People Using Stacking Ensemble Learning Framework Based on a Novel Brain Network

Kang, Qiaoju; Gao, Qiang; Song, Yu; Tian, Zekun; Yang, Yi; Mao, Zemin; Dong, Enzeng

doi:10.1109/jsen.2021.3108471

Cited by 15 publications

(3 citation statements)

References 34 publications

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“…Deep learning, as a special subset of machine learning, has achieved advanced results in various fields such as object monitoring, speech recognition, and natural language processing [7]. In BCI systems, multi-channel EEG signals contain rich information that can be automatically analyzed for this specific type of data through deep learning, providing valuable output for the new generation of BCI systems [8]. proposed a combined deep learning architecture based on a detailed analysis of EEG in measuring anesthesia depth.…”

Section: Related Workmentioning

confidence: 99%

Analysis of EEG Signals Fused With CNN and Complex Networks

Ma,

2024

IEEE Access

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Currently, brain-computer interface technology still poses hidden dangers in the complete control of ideas. Additionally, there are issues with the low sampling frequency and accuracy of EEG signal acquisition equipment. To address these concerns, this study proposes a combined model for EEG signal recognition and classification analysis by combining frequency division multi-feature complex brain networks with parallel convolutional neural networks. The effectiveness of this model has been verified. In model visualization analysis, the visualization results of t-distribution random neighborhood embedding in the third separable convolutional layer indicate that the two types of imagination have already experienced separation. There is a clear boundary between the two at position 0 on both the horizontal and vertical axes. This is a significant improvement compared to the comparative model. In the model performance verification, the full band classification accuracy in the synchronous network was maintained between 60% to 84%, and the μ-rhythm was maintained between 59% to 81%. The average classification accuracy of the combined model was 77.40% with higher performance, which was higher than 68.53% and 70.87% of the single scale convolutional neural network. In comparison with deep learning algorithms, the average classification accuracy of the combined model was 85.74%, much higher than the 66.20% and 76.69% of the comparative models. The composite model constructed has good performance in recognizing and classifying electroencephalogram signals. It can be effectively applied in practical brain-computer interface technology or electroencephalogram signal analysis.

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Section: Related Workmentioning

confidence: 99%

Analysis of EEG Signals Fused With CNN and Complex Networks

Ma,

2024

IEEE Access

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“…Due to the loss of a key channel during the process of emotion communication, the individuals with hearing impairment can only compensate for changes in the outside world through senses such as vision and touch. Therefore, the individuals with hearing impairment are more sensitive to emotional perception, and may have differences in recognition of emotion from healthy controls [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition of Subjects With Hearing Impairment Based on Fusion of Facial Expression and EEG Topographic Map

Liu

Yang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Emotion analysis has been employed in many fields such as human-computer interaction, rehabilitation, and neuroscience. But most emotion analysis methods mainly focus on healthy controls or depression patients. This paper aims to classify the emotional expressions in individuals with hearing impairment based on EEG signals and facial expressions. Two kinds of signals were collected simultaneously when the subjects watched affective video clips, and we labeled the video clips with discrete emotional states (fear, happiness, calmness, and sadness). We extracted the differential entropy (DE) features based on EEG signals and converted DE features into EEG topographic maps (ETM). Next, the ETM and facial expressions were fused by the multichannel fusion method. Finally, a deep learning classifier CBAM_ResNet34 combined Residual Network (ResNet) and Convolutional Block Attention Module (CBAM) was used for subject-dependent emotion classification. The results show that the average classification accuracy of four emotions recognition after multimodal fusion achieves 78.32%, which is higher than 67.90% for facial expressions and 69.43% for EEG signals. Moreover, visualization by the Gradient-weighted Class Activation Mapping (Grad-CAM) of ETM showed that the prefrontal, temporal and occipital lobes were the brain regions closely related to emotional changes in individuals with hearing impairment.

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“…However, the base learners of Boosting and Bagging ensemble learning are generally generated by the same learning algorithm, which cannot reflect the advantages of different algorithms. Stacking ensemble methods are applied in android malware detection [11] and emotion recognition [12], etc., which can effectively integrate different kinds of base learners, thus effectively improving the prediction accuracy. However, the selection of base learners has a large impact on the prediction results of Stacking ensemble models, and the performance of poor base learners can easily affect the combined results if the performance of base learners differs significantly.…”

Section: Introductionmentioning

confidence: 99%

A Study on House Price Prediction Based on Stacking-Sorted-Weighted-Ensemble Model

Li¹,

Li²,

Xie³

et al. 2022

Journal of Internet Technology

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<p>The house price prediction problem is a typical regression problem, and most of the common house price prediction models are single prediction algorithms, which are not ideal in terms of accuracy and stability. For solving this problem, this paper proposes a house price forecasting method based on Stacking-Sorted-Weighted-Ensemble (SSWE) model. Considering the characteristics of different algorithms and giving full play to the advantages of each model, multiple individual forecasting models are fused with the Stacking model. The algorithm validation is performed using the data generated by the system of real estate management department in western Guangdong. The prediction results show that the Stacking model is superior to the single model. Compared with the Stacking regression model, the SSWE model has a 13.6% increase in the root mean square error on the training set but a 0.3% decrease on the test set, indicating that the SSWE model prevents overfitting to a some extent and increases the accuracy and stability of the model.</p> <p> </p>

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Emotion Recognition From EEG Signals of Hearing-Impaired People Using Stacking Ensemble Learning Framework Based on a Novel Brain Network

Cited by 15 publications

References 34 publications

Analysis of EEG Signals Fused With CNN and Complex Networks

Analysis of EEG Signals Fused With CNN and Complex Networks

Emotion Recognition of Subjects With Hearing Impairment Based on Fusion of Facial Expression and EEG Topographic Map

A Study on House Price Prediction Based on Stacking-Sorted-Weighted-Ensemble Model

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