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<p>This study investigates the application of virtual reality technology in the creative expression of digital film and television productions, especially the role of EEG signal denoising and feature recognition methods in enhancing the audience experience. The study uses wavelet threshold denoising and parallel RLS adaptive filtering algorithms to process EEG signals to improve the accuracy and reliability of the data. Then, the EEG signals were feature extracted using a bihemispheric domain adversarial neural network (BiDANN) to more accurately recognize the user’s emotional responses. The experimental results show that in the virtual reality environment, the users’ concentration and emotional reactions are significantly improved, with the average concentration reaching 74.21 and the average value of the electrodermal test data being 6.19. In addition, the eye-movement interaction experiments show that different types of digital movie and television works can cause additional attention allocation of users in the VR environment, leading other creative performance effects. The study’s results prove that virtual reality technology can significantly enhance the innovative performance of digital movie and television works and improve the audience’s viewing experience.</p>
</abstract>

This study investigates the application of virtual reality technology in the creative expression of digital film and television productions, especially the role of EEG signal denoising and feature recognition methods in enhancing the audience experience. The study uses wavelet threshold denoising and parallel RLS adaptive filtering algorithms to process EEG signals to improve the accuracy and reliability of the data. Then, the EEG signals were feature extracted using a bihemispheric domain adversarial neural network (BiDANN) to more accurately recognize the user’s emotional responses. The experimental results show that in the virtual reality environment, the users’ concentration and emotional reactions are significantly improved, with the average concentration reaching 74.21 and the average value of the electrodermal test data being 6.19. In addition, the eye-movement interaction experiments show that different types of digital movie and television works can cause additional attention allocation of users in the VR environment, leading other creative performance effects. The study’s results prove that virtual reality technology can significantly enhance the innovative performance of digital movie and television works and improve the audience’s viewing experience.

Research on the Creative Performance of Digital Film and Television Works Based on Virtual Reality Technology

Applied Mathematics and Nonlinear Sciences

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

This study investigates the application of virtual reality technology in the creative expression of digital film and television productions, especially the...

Research on the Creative Performance of Digital Film and Television Works Based on Virtual Reality Technology

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 15 janv. 2024

Accepté: 25 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0633

Mots clés
Virtual reality technology, Digital film and television works, EEG denoising, Feature recognition, Creative performance

© 2023 Jicheng Zhang et al., published by Sciendo

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

Research on the Creative Performance of Digital Film and Television Works Based on Virtual Reality Technology

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 15 janv. 2024

Accepté: 25 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0633

Mots clésVirtual reality technology, Digital film and television works, EEG denoising, Feature recognition, Creative performance

© 2023 Jicheng Zhang et al., published by Sciendo

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

Mots clés
Virtual reality technology, Digital film and television works, EEG denoising, Feature recognition, Creative performance