Dream Recording Through Non-invasive Brain-Machine Interfaces and Generative AI-assisted Multimodal Software

Abstract

The present study proposes a novel approach to dream recording by combining non-invasive brain-machine interfaces (BMI), thought-typing software, and generative AI-assisted multimodal software. This method aims to sublimate conscious processes into semi-conscious status during REM sleep and produce signals for thought typing. We outline a two-stage process: first, developing multimodal software using generative AI to supplement text streams and generate multimedia content; second, adapting Morse code-based typing to simplify signal requirements and increase typing speed. We address the challenge of non-invasive EEG by suggesting a control system involving a user with an implanted BMI to optimize non-invasive signals. A literature review highlights recent advancements in BMI typing, sublimation of conscious processes, and generative AI's potential in thought typing based on text prompts.