Scene-EEGCNN: Visualization of Zen Meditation Experience Based on EEG-Cultural Heritage Integration

Abstract

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.