Improving P300 Spelling Rate using Language Models and Predictive Spelling
| Autor: | Nand Chandravadia, William Speier, Dustin G. Roberts, Shrita Pendekanti, Corey W. Arnold, Nader Pouratian |
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| Rok vydání: | 2018 |
| Předmět: |
predictive spelling
Interface (Java) Computer science 0206 medical engineering Biomedical Engineering 02 engineering and technology computer.software_genre Article 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine P300 speller language models Prior probability Selection (linguistics) Electrical and Electronic Engineering business.industry Neurosciences Electroencephalography 020601 biomedical engineering Spelling Human-Computer Interaction Brain-Computer Interfaces Language model Artificial intelligence business computer Classifier (UML) 030217 neurology & neurosurgery Natural language processing Word (computer architecture) Natural language |
| Zdroj: | Brain computer interfaces (Abingdon, England), vol 5, iss 1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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