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.

翻译：肌萎缩侧索硬化症（ALS）严重损害患者的交流能力，通常在确诊后数年内导致其生活质量下降。P300拼写器脑机接口通过解读被试对网格界面呈现字符的脑电响应，提供了一种替代性交流方式。本文针对训练高效P300多被试分类器时普遍存在的速度限制问题，引入了创新的"跨被试"分类器。我们结合第二代生成式预训练Transformer（GPT2）与Dijkstra算法来优化刺激呈现，并根据输入历史推荐单词补全选项。此外，采用多层平滑技术以处理超纲词汇。通过对被试脑电数据进行随机抽样的大规模仿真实验，我们证实在输入包含罕见词和超纲词的文本段落时，系统能实现显著的输入速度提升。这些优化使字符级输入速度提升约10%，多词预测性能提升最高达40%。实验表明，在标准行列高亮技术基础上叠加分层单词预测，可获得接近最优的性能表现。我们进一步探究了"被试内"与"跨被试"两种训练技术，证明速度提升在两种训练模式下均能保持稳定。