扩散:基于脑电生物反馈控制的情绪可视化

IF 0.2 0 HUMANITIES, MULTIDISCIPLINARY
Shuai Xu, Zhe Wang
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引用次数: 1

摘要

扩散是一个研究领域,通过分析基于脑电图(EEG)的脑机接口和可以通过生物反馈产生音乐和同步视觉图像的交互设备的人脑数据,探索人类意识和计算实践之间的交互关系。虽然互动体验在计算艺术中并不是一个新话题,但由于技术对人类意识形态、情感、道德、伦理等方面的重大影响,它引发了人们的思考。扩散是试图在人类生理信息和数字技术之间建立联系的结果。除了实验研究,它还基于人工智能(AI)的伦理水平。扩散利用音乐可视化将无形的大脑活动(思想或情感)转化为可感知的事物(声音或物体)。该研究强调了人工智能中的人类意识,指出了人工智能与人类创造力之间模糊的界限。因此,装置对人的动机进行了评估,这种动机可以表现为抽象的结构,如创造力、情感和洞察力,增强了参与者的互动体验,解构了物质与精神、现实与虚拟现实或人与机器的内在意义。通过对脑电图和数字音乐发展研究的回顾和背景分析,我们最终勾勒出一个未来的研究领域,该领域将涉及跨学科和多种技术的深度合作,以实现情感识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diffusion: Emotional Visualization Based on Biofeedback Control by EEG
Diffusion is an area of research exploring the interactive relationship between human consciousness and computational practice by analyzing human brain data from electroencephalogram (EEG)-based brain-computer interfaces and interactive devices that can generate music and synchronized visual images by biofeedback. Although interactive experience is not a new topic in computational art, it has provoked thought due to the significant influence of technology on human ideology, emotions, morality, ethics, etc. Diffusion is the result of attempts to establish a connection between human physiological information and digital technology. As well as experimental research, it based on the ethical level of artificial intelligence (AI). Diffusion uses music visualization to transform intangible brain activity (thoughts or emotions) into perceivable things (sounds or objects). The research emphasizes human consciousness in AI and points out the blurred boundaries between AI and human creativity. Therefore, the installation evaluates human motivation, which can present as abstract structures—like creativity, emotion, and insight—which enhance the interactive experience of participants and deconstructs the inherent meaning of the material and spiritual, reality and virtual reality or humans and machines. By reviewing and contextualizing EEG and digital music development research, we finally outline a future research area that will involve deep collaboration across interdisciplinary and multiple technologies to realize emotion recognition.
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来源期刊
Artnodes
Artnodes HUMANITIES, MULTIDISCIPLINARY-
CiteScore
0.70
自引率
0.00%
发文量
26
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