In the context of intensifying global cultural exchanges, Zen culture---emblematic of traditional Chinese heritage---has increasingly captured the attention of the modern world. Zazen, the fundamental practice of Zen, yet the emotional transitions experienced during Zazen are not easily discernible or representable. This study introduces a novel approach to portraying the internal experiences of practitioners by leveraging scenes inspired by Zen. Elements of Chinese cultural heritage, such as Buddhist sculptures, poetry, and landscape paintings, serve as a rich repository for crafting these Zen scenes. We propose a methodology that utilizes EEG data to analyze emotional shifts and correlates these findings with traditional Chinese cultural elements, employing virtual scenes to depict the emotional journey of a Zazen practitioner. Specifically, we present the Scene-EEGCNN algorithm, which captures and interprets EEG signals in real time to evaluate the practitioner's emotional state and internal dynamics. Given the challenge of externally recognizing the emotional shifts in a Zazen practitioner, our algorithm correlates the emotional data with specific facets of Zen culture, creating a virtual scene that intuitively portrays the practitioner's inner landscape. This technology enables them to share their Zen meditation experiences visually, thereby enhancing global cultural exchange.

As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for intangible cultural heritage preservation. However, this craft confronts existential threats from digital entertainment proliferation and practitioner attrition. To address these challenges, this study proposes CSPMotifsGAN, an enhanced CycleGAN framework for constructing a motif dataset through three-stage processing: adaptive denoising, hierarchical classification, and multi-branch feature extraction (contour, texture, color). By integrating adversarial loss, cycle-consistency loss, and identity preservation loss, the model effectively resolves color distortion and textural degradation inherent in conventional CycleGAN. Experimental results demonstrate significant improvements: Fréchet Inception Distance(FID), Peak Signal-to-Noise Ratio(PSNR), and Structural Similarity Index(SSIM), validated through both subjective evaluations and statistical analysis.

The particular characteristics of deaf-mutes make them more likely to have mental health problems. Due to their particular way of communication, it is more difficult for them to deal with mental health problems than ordinary people. Nowadays, those psychologists who are good at sign language are in short supply, and remote assistance cannot achieve satisfactory results. Therefore, a library of virtual emotional gestures based on electroencephalogram(EEG) was established and a prototype system for mental health examination of deaf-mutes was proposed, which help deaf-mutes identify their psychological problems in time and assist medical staff to examine the psychological problems encountered by deaf-mutes. In addition, the virtual library of emotional gestures is established with the assistance of the chief physician from a 3A hospital in Henan Province. More importantly, the later experiments demonstrate the applicability of this virtual system.