Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection

Chen, Jing; Li, Haifeng; Ma, Lin; Bo, Hongjian

doi:10.4018/ijmdem.2021040101

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e EEMD algorithm adds enough normal distributed white noise into the original time series [21] and meanwhile performs EMD decomposition on the new time series. en, the arithmetic average of each decomposition quantity is taking advantage of the property that the mean value of white noise is zero, and the IMF component and the remaining term R of EEMD decomposition can be obtained [22]. e decomposition steps are as follows:…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Jiang

Xiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Ideological and political education is the most important way to cultivate students’ humanistic qualities, which can directly determine the development of other qualities. However, at present, the direction of ideological and political innovation in higher vocational colleges is vague. In response to this problem, this study proposes a model based on HS-EEMD-RNN. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the measured values, and then the recurrent neural network (RNN) is used to train each component and the remaining items. Finally, through the mapping relationship obtained by the model, the response prediction value of each component and the remaining items can be obtained. In the RNN training process, the harmony search (HS) algorithm is introduced to optimize it, and the noise is systematically denoised. Perturbation is used to obtain the optimal solution, thereby optimizing the weight and threshold of the RNN and improving the robustness of the model. The study found that, compared with EEMD-RNN, HS-EEMD-RNN has a better effect, because HS can effectively improve the training and fitting accuracy. The fitting accuracy of the HS-EEMD-RNN model after HS optimization is 0.9918. From this conclusion, the fitting accuracy of the HS-EEMD-RNN model is significantly higher than that of the EEMD-RNN model. In addition, four factors, career development, curriculum construction, community activities, and government support, have obvious influences on ideological and political classrooms in technical colleges. The use of recurrent neural networks in the research direction of deep and innovative research on the subject context of ideological and political classrooms can significantly improve the prediction accuracy of its development direction.

show abstract

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Jiang

Xiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…This text can be a sentence, a paragraph, or even a document. Text classification is also an important part of docking downstream tasks such as information retrieval [2], topic division [3], and question-answering systems [4] in the field of NLP. As one of the complex scenarios in text classification, Multi-Label Text Classification (MLTC) needs to take into account the correlation between text feature extraction and mining labels.…”

Section: Introductionmentioning

confidence: 99%

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

Cheng,

Chen,

et al. 2023

CMC

View full text Add to dashboard Cite

In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model's grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model's capacity to understand and represent the data. Additionally, to address the intricate task of mining label correlations, we propose a Dual-end enhancement mechanism. This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation. We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets. These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism. Our final model unequivocally outperforms the baseline model, attesting to its robustness. These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.

show abstract

Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection

Cited by 2 publications

References 26 publications

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

Contact Info

Product

Resources

About