<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the emotions of dancers are identified in combination with the integrated deep-learning model. Firstly, four initial value features with important emotional states are extracted from the time, frequency, and time-frequency domains, respectively. It was isolated using a deep belief network enhanced by neuro colloidal chains. Finally, the finite Boltzmann criterion integrates the features of higher abstractions and predicts the emotional states. The results of DEAP data show that the correlation between EEG channels can be discovered and applied by glial chains. The fused deep learning model combines EEG emotional features with temporal, frequency, and expressive qualities.</p>
</abstract>

In this paper, the emotions of dancers are identified in combination with the integrated deep-learning model. Firstly, four initial value features with important emotional states are extracted from the time, frequency, and time-frequency domains, respectively. It was isolated using a deep belief network enhanced by neuro colloidal chains. Finally, the finite Boltzmann criterion integrates the features of higher abstractions and predicts the emotional states. The results of DEAP data show that the correlation between EEG channels can be discovered and applied by glial chains. The fused deep learning model combines EEG emotional features with temporal, frequency, and expressive qualities.

Dance Emotion Characteristic Parameters Based on Deep Learning Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Dance Emotion Characteristic Parameters Based on Deep Learning Model"}

In this paper, the emotions of dancers are identified in combination with the integrated deep-learning model. Firstly, four initial value features with...

Dance Emotion Characteristic Parameters Based on Deep Learning Model

Online veröffentlicht: 21. Juni 2023

Seitenbereich: 2599 - 2606

Eingereicht: 05. Sept. 2022

Akzeptiert: 26. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00440

Schlüsselwörter
Dance emotion recognition, Multichannel EEG, Deep learning, Deep belief network, Feature fusion, Restricted Boltzmann machine

© 2023 Hongyun Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Dance Emotion Characteristic Parameters Based on Deep Learning Model

Online veröffentlicht: 21. Juni 2023

Seitenbereich: 2599 - 2606

Eingereicht: 05. Sept. 2022

Akzeptiert: 26. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00440

SchlüsselwörterDance emotion recognition, Multichannel EEG, Deep learning, Deep belief network, Feature fusion, Restricted Boltzmann machine

© 2023 Hongyun Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Dance emotion recognition, Multichannel EEG, Deep learning, Deep belief network, Feature fusion, Restricted Boltzmann machine