On using AI for EEG-based BCI applications: problems, current challenges and future trends

TL;DR

This paper reviews the current state, challenges, and future directions of applying AI to EEG-based brain-computer interfaces, emphasizing the need for reliable, practical solutions for real-world use.

Contribution

It provides a principled overview of the research landscape, highlighting fundamental paradigms, challenges, and promising avenues for advancing AI-driven EEG-based BCIs.

Findings

Identifies key challenges in deploying AI for real-world EEG BCIs.

Discusses foundational models and paradigms from a causal perspective.

Outlines future research directions to overcome technological and ethical hurdles.

Abstract

Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling exciting progress in decoding brain signals from scalp electroencephalography (EEG). Prima facie, this opens the door to revolutionary brain-computer interfaces (BCIs) designed for real life, moving beyond traditional uses to envision Brain-to-Speech, Brain-to-Image, and even a Brain-to-Internet of Things (BCIoT). However, the journey is not as straightforward as it was for Computer Vision (CV) and Natural Language Processing (NLP). Applying AI to real-world EEG-based BCIs, particularly in building powerful foundational models, presents unique and intricate hurdles that could affect their reliability. Here, we unfold a guided exploration of this dynamic and rapidly evolving research area. Rather than barely outlining a map of current endeavors and results, the goal is to provide a principled navigation of this hot and cutting-edge research landscape. We consider the basic paradigms that emerge from a causal perspective and the attendant challenges presented to AI-based models. Looking ahead, we then discuss promising research avenues that could overcome today's technological, methodological, and ethical limitations. Our aim is to lay out a clear roadmap for creating truly practical and effective EEG-based BCI solutions that can thrive in everyday environments.