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

2025-06-19

AI Summary

This study addresses critical bottlenecks hindering real-world deployment of AI-driven scalp EEG-based brain–computer interfaces (BCIs): low reliability, poor generalizability, scarce labeled data, and weak interpretability. To tackle these challenges, we first establish a causal-paradigm-based evaluation framework for foundational EEG–BMI models. We then propose a unified methodology integrating causal inference, few-shot learning, neural representation modeling, and cross-subject transfer—specifically tailored to EEG’s low signal-to-noise ratio and high inter-individual variability. Furthermore, we systematically identify key technical gaps in emerging domains such as brain-to-speech and brain-to-image translation. Finally, we articulate a robust, generalizable, and ethically aligned development roadmap for BCI-enabled Internet-of-Things (BCIoT) systems, offering principled design guidelines for practical EEG interfaces. This work bridges theoretical advances in causal and representation learning with pragmatic requirements of clinical and consumer-grade neurotechnology.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Causal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Decoding EEG brain signals for real-life BCIs using AI
• Overcoming reliability hurdles in AI-based EEG BCI models
• Addressing technological and ethical limits in EEG BCI solutions

Innovation

• AI decodes EEG signals for BCIs
• Foundational models tackle EEG reliability
• Causal perspective guides BCI research