AI Agents in Intracortical Brain-Computer Interfaces: A Comprehensive Review and Proposed Integration Approach

Abstract

Intracortical brain-computer interfaces (iBCIs) such as those demonstrated by Neuralink have shown significant potential in enabling direct communication between the human brain and external devices. However, the complexity and high dimensionality of neural data pose challenges in interpreting and translating brain activity into meaningful commands. This paper presents a comprehensive review of the current state of iBCIs, including advanced signal acquisition and decoding techniques, and explores the limitations of traditional approaches in achieving seamless brain-machine interaction. We propose a novel approach that leverages advanced AI agents, equipped with capabilities such as reflection, hierarchical planning, and decision-making, as an interface between the brain and iBCIs. By incorporating these advanced AI techniques, we aim to enhance the interpretation of neural signals, improve the efficiency of task execution, and provide a more intuitive and adaptable user experience to achieve goal-oriented outcomes from thoughts. The proposed approach is discussed in detail, highlighting its potential benefits and the challenges that need to be addressed. We conclude by outlining future research directions and the prospects of integrating advanced AI agents with iBCIs for various applications, including neurorehabilitation, assistive technologies, and human augmentation.