MindGPT: Advancing Human-AI Interaction with Non-Invasive fNIRS-Based Imagined Speech Decoding

TL;DR

This paper introduces MindGPT, a pioneering system that decodes imagined speech using non-invasive fNIRS technology, enabling more natural and seamless human-AI communication.

Contribution

It presents the first thought-to-LLM system utilizing non-invasive fNIRS for imagined speech decoding, advancing human-AI interaction capabilities.

Findings

Successful decoding of imagined speech signals.

First implementation of a thought-to-LLM system.

Potential for improved human-AI communication.

Abstract

In the coming decade, artificial intelligence systems are set to revolutionise every industry and facet of human life. Building communication systems that enable seamless and symbiotic communication between humans and AI agents is increasingly important. This research advances the field of human-AI interaction by developing an innovative approach to decode imagined speech using non-invasive high-density functional near-infrared spectroscopy (fNIRS). Notably, this study introduces MindGPT, the first thought-to-LLM (large language model) system in the world.