Probabilistic Simulation Framework for EEG-Based BCI Design.

A simulation framework could decrease the burden of attending long and tiring experimental sessions on the potential users of brain computer interface (BCI) systems. Specifically during the initial design of a BCI, a simulation framework that could replicate the operational performance of the system would be a useful tool for designers to make design choices. In this manuscript, we develop a Monte Carlo based probabilistic simulation framework for electroencephalography (EEG) based BCI design.

Graph-Based Word Alignment for Clinical Language Evaluation.

Among the more recent applications for natural language processing algorithms has been the analysis of spoken language data for diagnostic and remedial purposes, fueled by the demand for simple, objective, and unobtrusive screening tools for neurological disorders such as dementia. The automated analysis of narrative retellings in particular shows potential as a component of such a screening tool since the ability to produce accurate and meaningful narratives is noticeably impaired in individuals with dementia and its frequent precursor, mild cognitive impairment, as well as other neurodegenerative and neurodevelopmental disorders. In this article, we present a method for extracting narrative recall scores automatically and highly accurately from a word-level alignment between a retelling and the source narrative.

Huffman and linear scanning methods with statistical language models.

Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing.

COMPUTATIONAL ANALYSIS OF TRAJECTORIES OF LINGUISTIC DEVELOPMENT IN AUTISM.

Deficits in semantic and pragmatic expression are among the hallmark linguistic features of autism. Recent work in deriving computational correlates of clinical spoken language measures has demonstrated the utility of automated linguistic analysis for characterizing the language of children with autism. Most of this research, however, has focused either on young children still acquiring language or on small populations covering a wide age range.

Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

Background: Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation.

Objective: To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion.

Methods: The RSVP Keyboard was developed with several unique features.

Huffman scanning: using language models within fixed-grid keyboard emulation.

Individuals with severe motor impairments commonly enter text using a single binary switch and symbol scanning methods. We present a new scanning method -Huffman scanning - which uses Huffman coding to select the symbols to highlight during scanning, thus minimizing the expected bits per symbol. With our method, the user can select the intended symbol even after switch activation errors.

Offline analysis of context contribution to ERP-based typing BCI performance.

Objective: We aim to increase the symbol rate of electroencephalography (EEG) based brain-computer interface (BCI) typing systems by utilizing context information.

Approach: Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems.

Distributional semantic models for the evaluation of disordered language.

Atypical semantic and pragmatic expression is frequently reported in the language of children with autism. Although this atypicality often manifests itself in the use of unusual or unexpected words and phrases, the rate of use of such unexpected words is rarely directly measured or quantified. In this paper, we use distributional semantic models to automatically identify unexpected words in narrative retellings by children with autism.

The application of natural language processing to augmentative and alternative communication.

Significant progress has been made in the application of natural language processing (NLP) to augmentative and alternative communication (AAC), particularly in the areas of interface design and word prediction. This article will survey the current state-of-the-science of NLP in AAC and discuss its future applications for the development of next generation of AAC technology.

Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers.

Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed.

RSVP Keyboard: An EEG Based Typing Interface.

Humans need communication. The desire to communicate remains one of the primary issues for people with locked-in syndrome (LIS). While many assistive and augmentative communication systems that use various physiological signals are available commercially, the need is not satisfactorily met.

Spoken Language Derived Measures for Detecting Mild Cognitive Impairment.

Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI).

Context Information Significantly Improves Brain Computer Interface Performance - a Case Study on Text Entry Using a Language Model Assisted BCI.

We present recent results on the design of the RSVP Keyboard - a brain computer interface (BCI) for expressive language generation for functionally locked-in individuals using rapid serial visual presentation of letters or other symbols such as icons. The proposed BCI design tightly incorporates probabilistic contextual information obtained from a language model into the single or multi-trial event related potential (ERP) decision mechanism. This tight fusion of contextual information with instantaneous and independent brain activity is demonstrated to potentially improve accuracy in a dramatic manner.

Designing antimicrobial peptides with weighted finite-state transducers.

The design of novel antimicrobial peptides (AMPs) is an important problem given the rise of drug-resistant bacteria. However, the large size of the sequence search space, combined with the time required to experimentally test or simulate AMPs at the molecular level makes computational approaches based on sequence analysis attractive. We propose a method for designing novel AMPs based on learning from n-gram counts of classes of amino acid residues, and then using weighted finite-state machines to produce sequences that incorporate those features that are strongly associated with AMP sequences.