ChatBCI-4-ALS: A High-Performance, LLM-Driven, Intent-Based BCI Communication System for Individuals with ALS

Jiazhen Hong, Pradyumna Rao, Weinan Wang, Shan Chen, Laleh Najafizadeh

Abstract: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that leads to significant motor and speech impairments, increasing the need for alternative means of communication to support quality of life. P300 speller brain computer interfaces (BCIs) have shown promise in facilitating non-muscular communication by detecting P300 event-related potentials (ERPs) in response to visual stimuli. However, these systems are generally slow and can not fully address the communication needs of ALS patients, specially, when the primary goal is to convey intent with minimal cognitive load. In this paper, we present ChatBCI-4-ALS, the first intent-based BCI communication system designed for individuals with ALS. ChatBCI-4-ALS leverages large language models (LLMs) and employs a dynamic flash algorithm to enhance typing speed, and enable efficient communication of the user’s intent beyond exact lexical matches. Additionally, we introduce new semantic-based quantitative performance metrics to evaluate the effectiveness of intent-based communication. Results from online experiments suggest that ChatBCI-4-ALS achieves record-breaking average spelling speed of 23.87 char/min (with the best case scenario of 42.16 char/min), and a best information transfer rate (ITR) of 128.85 bits/min, marking an advancement in P300 BCI-based communication systems.