Speier William, Arnold Corey, Chandravadia Nand, Roberts Dustin, Pendekanti Shrita, Pouratian Nader
Department of Neurosurgery, University of California, Los Angeles, USA.
Medical Imaging Informatics Group, University of California, Los Angeles, USA.
Brain Comput Interfaces (Abingdon). 2018;5(1):13-22. doi: 10.1080/2326263X.2017.1410418. Epub 2017 Dec 26.
The P300 Speller Brain-Computer Interface (BCI) provides a means of communication for those suffering from advanced neuromuscular diseases such as amyotrophic lateral sclerosis (ALS). Recent literature has incorporated language-based modelling, which uses previously chosen characters and the structure of natural language to modify the interface and classifier. Two complementary methods of incorporating language models have previously been independently studied: predictive spelling uses language models to generate suggestions of complete words to allow for the selection of multiple characters simultaneously, and language model-based classifiers have used prior characters to create a prior probability distribution over the characters based on how likely they are to follow. In this study, we propose a combined method which extends a language-based classifier to generate prior probabilities for both individual characters and complete words. In order to gauge the efficiency of this new model, results across 12 healthy subjects were measured. Incorporating predictive spelling increased typing speed using the P300 speller, with an average increase of 15.5% in typing rate across subjects, demonstrating that language models can be effectively utilized to create full word suggestions for predictive spelling. When combining predictive spelling with language model classification, typing speed is significantly improved, resulting in better typing performance.
P300 拼写脑机接口（BCI）为患有诸如肌萎缩侧索硬化症（ALS）等晚期神经肌肉疾病的患者提供了一种交流方式。最近的文献引入了基于语言的建模方法，该方法利用先前选择的字符和自然语言的结构来修改接口和分类器。此前，两种结合语言模型的互补方法已被分别独立研究：预测拼写使用语言模型生成完整单词的建议，以便同时选择多个字符；基于语言模型的分类器则利用先前的字符，根据字符后续出现的可能性创建字符上的先验概率分布。在本研究中，我们提出了一种组合方法，该方法扩展了基于语言的分类器，以生成单个字符和完整单词的先验概率。为了评估这个新模型的效率，我们测量了 12 名健康受试者的结果。结合预测拼写提高了使用 P300 拼写器的打字速度，受试者的打字速率平均提高了 15.5%，这表明语言模型可以有效地用于为预测拼写创建完整单词建议。当将预测拼写与语言模型分类相结合时，打字速度显著提高，从而带来更好的打字性能。
