High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training

TL;DR

This paper introduces a novel P300 speller BCI system that combines GPT2-based word prediction and cross-subject training to significantly enhance typing speed, especially for rare and out-of-vocabulary words.

Contribution

It presents an innovative across-subject classifier using GPT2 and Dijkstra's algorithm, improving P300 speller speed and efficiency for multi-subject applications.

Findings

Approximately 10% increase in character-level typing speed.

Up to 40% improvement in multi-word prediction accuracy.

Consistent speed improvements across within- and across-subject training.

Abstract

Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain-computer interface (BCI) offers an alternative communication method by interpreting a subject's EEG response to characters presented on a grid interface. This paper addresses the common speed limitations encountered in training efficient P300-based multi-subject classifiers by introducing innovative "across-subject" classifiers. We leverage a combination of the second-generation Generative Pre-Trained Transformer (GPT2) and Dijkstra's algorithm to optimize stimuli and suggest word completion choices based on typing history. Additionally, we employ a multi-layered smoothing technique to accommodate out-of-vocabulary (OOV) words. Through extensive simulations involving random sampling of EEG data from subjects, we demonstrate significant speed enhancements in typing passages containing rare and OOV words. These optimizations result in approximately 10% improvement in character-level typing speed and up to 40% improvement in multi-word prediction. We demonstrate that augmenting standard row/column highlighting techniques with layered word prediction yields close-to-optimal performance. Furthermore, we explore both "within-subject" and "across-subject" training techniques, showing that speed improvements are consistent across both approaches.