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.

该研究论文通过创新的 “跨主体” 分类器解决基于 P300 的多主体分类器训练中遇到的常见速度限制问题，并通过使用第二代 GPT2 和 Dijkstra 算法来优化刺激并根据打字历史建议单词补全选择，以及使用多层平滑技术来适应词汇表以外的词汇，通过广泛模拟从受试者中随机抽样的脑电图数据，证明了在包含罕见和词汇表以外词汇的打字段落中，显著提高了打字速度约 10％的改进，并在多词预测中提高了多达 40％的改进。研究证明，将传统的行 / 列突出显示技术与分层单词预测相结合可以实现接近最优的性能。此外，研究还探讨了 “同主体” 和 “跨主体” 训练技术，结果表明速度的改进在这两种方法中都是一致的。

基于 GPT2 词语预测与跨受试者训练的高性能 P300 拼写器

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

Path finding in graphs is one of the most studied classes of problems in
computer science. In this context, search algorithms are often extended with
heuristics for a more efficient search of target nodes. In this work we combine
recent advancements in Neural Algorithmic Reasoning to learn efficient
heuristic functions for path finding problems on graphs. At training time, we
exploit multi-task learning to learn jointly the Dijkstra's algorithm and a
consistent heuristic function for the A* search algorithm. At inference time,
we plug our learnt heuristics into the A* algorithm. Results show that running
A* over the learnt heuristics value can greatly speed up target node searching
compared to Dijkstra, while still finding minimal-cost paths.

本文利用神经演算法推导出高效的启发式路径搜索算法，结合 Dijkstra 算法和 A * 算法中的一致的启发函数，并将其推广应用于图形中的路径搜索问题，结果表明相较于 Dijkstra 算法，应用基于学习到的 A * 算法中的启发函数的改进搜索在无损质量的情况下，实现了大幅的速度提升。