An Innovative Brain-Computer Interface Interaction System Based on the Large Language Model

2025-02-17

AI Summary

Current brain–computer interfaces (BCIs) suffer from limited functionality, rigid paradigms, poor multilingual support, and insufficient intelligence. To address these limitations, this work proposes the first intelligent BCI system that deeply integrates a steady-state visual evoked potential (SSVEP) speller with large language model (LLM) APIs. Our method introduces novel LLM-driven capabilities: dynamic SSVEP paradigm generation, natural-language instruction parsing, and cross-task decision-making. The system incorporates SSVEP decoding, adaptive paradigm synthesis, multilingual prompt engineering, and cross-device interface adaptation—enabling operation across over ten languages and diverse application scenarios including home appliance control, robotic arm manipulation, and drone management. Experimental results demonstrate significant improvements in interaction naturalness, paradigm configurability, and task extensibility, along with substantial gains in classification accuracy and user satisfaction.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Recent advancements in large language models (LLMs) provide a more effective pathway for upgrading brain-computer interface (BCI) technology in terms of user interaction. The widespread adoption of BCIs in daily application scenarios is still limited by factors such as their single functionality, restricted paradigm design, weak multilingual support, and low levels of intelligence. In this paper, we propose an innovative BCI system that deeply integrates a steady-state visual evoked potential (SSVEP) speller with an LLM application programming interface (API). It allows natural language input through the SSVEP speller and dynamically calls large models to generate SSVEP paradigms. The command prompt, blinking frequency, and layout position are adjustable to meet the user's control requirements in various scenarios. More than ten languages are compatible with the multilingual support of LLM. A variety of task scenarios, such as home appliance control, robotic arm operation, and unmanned aerial vehicle (UAV) management are provided. The task interfaces of the system can be personalized according to the user's habits, usage scenarios, and equipment characteristics. By combining the SSVEP speller with an LLM, the system solves numerous challenges faced by current BCI systems and makes breakthroughs in functionality, intelligence, and multilingual support. The introduction of LLM not only enhances user experience but also expands the potential applications of BCI technology in real-world environments.

Problem

• Enhance BCI user interaction using large language models
• Address BCI limitations in functionality and multilingual support
• Integrate SSVEP speller with LLM for dynamic paradigm generation

Innovation

• Integrates SSVEP speller with LLM API
• Supports multilingual natural language input
• Customizable task interfaces for diverse scenarios