P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.
Forward masking was investigated as a measure of spectral and temporal interactions. Such interactions may adversely affect speech recognition in cochlear-implant subjects. Seven subjects, implanted with the Nucleus 22 device, performed a forward-masking task. They also performed an electrode-discrimination task in order to measure spectral interactions without temporal interactions. Correlation analysis indicated a significant relationship between data obtained in the two tasks (p < 0.1). The two tasks were also correlated with the subjects' scores from five measures of speech recognition. Forward masking and electrode discrimination were strongly correlated with measures requiring consonant and phoneme recognition, respectively. These results indicate that the relationship between forward masking and speech recognition may be due, in part, to a lack of spectral resolution. The data also indicate that consonants may be more readily masked than vowels. Forward-masking data measured for all clinically programmed electrodes in three of the seven subjects were used with a model of the spectral maxima sound processor (SMSP) to estimate the number of electrodes stimulated during a consonant that might be masked by prior presentation of a vowel. These results suggest that temporal interactions across electrodes may be a factor in speech-recognition abilities of some cochlear-implant subjects.
Objective
The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred.
Approach
We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute signal-to-noise ratio of a user’s electroencephalography data. We further enhanced the algorithm by incorporating information about the user’s language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation.
Main Results
Results from online testing of the dynamic stopping algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/sec (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the dynamic stopping algorithms.
Significance
We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.
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