Hi all,
I am very new to the neurofeeback and EEG space, but have been fascinated with the potential in art, music and personal thought experimentation. I am not as technically savvy in terms of software dev and the maker space (I come from a more content-focused place), but I have a few project ideas I want to jump into and don’t know where to start.
I am interested in working with EEG devices and neurofeedback data to create artistic renderings of emotional or thought states in various personal and social situations (meditation, social outings with friends, sex, contemplating love, flow states, playing sports, etc). I’d also love to use live data for live art visualization (music visualizer that uses brainwaves while listening to music, playing video clips that are assigned to specific frequencies for shows, and even using multiple brainwave sources to create group art). Lastly, I would also love to create an “art” system that is focused on ego awareness and training in the practice of Presence and inner stillness. It would involve walking a user through engaging in thought tests to recognize ego processing and proceed through sentence prompts, cues and some NLP via fluctuations in brainwave activity (i.e lower beta).
I recognize that I may not need incredibly precise readings or too many channels, since I am not focused on the medical/professional level, but I would be totally wrong. I’ve seen a lot of love for the OpenBCI and Emotive, but I have been looking into Muse, Lowdown Focus, and Neurosky, mainly due to novice usability, available software (or ease of software dev), and access to raw data. I’d love one with good capability for live art use.
I am a newbie with big dreams. I know I may have to dive into some programming and maker work, but would like to start right so I am not overwhelmed. I do have friends in the neuroscience and machine learning spaces, but would prefer not to ask for TOO much help during this process.
Any recommendations on best device, software, and resources would be much appreciated.
thank you thank you thank you!