Abstract
Locked-in syndrome can result from traumatic brain injury, such as a brain-stem stroke, or from neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS or Lou Gehrig’s disease). The condition is characterized by near total paralysis, though the individuals are cognitively intact. While vision is retained, the motor control impairments extend to eye movements. Often the only reliable movement that can be made by an individual is a particular muscle twitch or single eye blink, if that. Typing interfaces for these populations are typically based on a binary response, via blinks, muscle twitches or (more recently) ERP signals captured through EEG signal processing. In this talk, I’ll discuss typing interfaces for impaired populations, with a particular focus on the role of language modeling within typing applications. I will contrast language modeling for binary switch response typing interfaces with the more standard use of language models for full sequence disambiguation in applications like speech recognition and machine translation, which has large implications for learning of such models. I will then highlight two key issues for construction of these language models: using Huffman coding versus simpler binary coding tree topologies; and handling of selection error within the model itself. I will present some language modeling results for two very large corpora: newswire text from the Gigaword corpus; and emails from the Enron corpus. I will also present experiments with a binary-switch, static-grid typing interface making use of varying language model contributions, using some novel scanning methods. (Joint work with Jacques de Villiers, Christopher Gibbons and Melanie Fried-Oken.)