Synesthesia-based Human Augmentation System for Brain to Brain Communication

Abstract

The brain-computer interface (BCI) is slowly coming close to us, but there are still many technical challenges. We still don’t know too much about the brain. To convert a pattern measured by the BCI to the information we can find out, we must find out how their neurons were firing in the brain, and what kind of pattern. The brain’s pattern information is incredibly complex. For BCI to be the target whole-brain BCI, it must be able to precisely capture every single neuron in the brain and transmit it at the rate at which the brain’s patterns ignite. In this paper, we proposed the core technology for communication between brains and brains instead of language-based communication by changing the communication method between humans from the existing communication methods (text, voice, video, etc.). In other words, we modeled synesthesia by analyzing the sense of the intention of the user to improve the efficiency of information transmission and respond to the objective action or emotional stimulus. We analyzed functional brain connectivity based on observational data on objective behavior. In this research, we visualized the brain connectivity and improved the way of expression by 89.05±1.96%. We proposed a synesthesia-based human augmentation system for brain to brain communication. 5-folds cross-validation based on functional brain connectivity was used to measure, predict and classify human responses in specific situations.