David B. Ryan

Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication.

This study compared a conventional P300 speller brain–computer interface (BCI) to one used in conjunction with a predictive spelling program. Performance differences in accuracy, bit rate, selections per minute, and output characters per minute (OCM) were examined. An 8 × 9 matrix of letters, numbers, and other keyboard commands was used. Participants (N = 24) were required to correctly complete the same 58 character sentence (i.e., correcting for errors) using the predictive speller (PS) and the nonpredictive speller (NS), counterbalanced. The PS produced significantly higher OCMs than the NS. Time to complete the task in the PS condition was 12 min 43 s as compared to 20 min 20 sec in the NS condition. Despite the marked improvement in overall output, accuracy was significantly higher in the NS paradigm. P300 amplitudes were significantly larger in the NS than in the PS paradigm, which is attributed to increased workload and task demands. These results demonstrate the potential efficacy of predictive spelling in the context of BCI.

Noninvasive brain-computer interface enables communication after brainstem stroke

An individual “locked-in” by a brainstem stroke was able to communicate using noninvasive brain-computer interface technology. Communicating with a Locked-in Patient “Locked-in” syndrome describes a patient who is awake, or conscious, but can’t communicate verbally; some patients even lack the ability to convey thoughts and emotions by movement, whether it be with a hand, a headshake, or the eyes. To help these patients communicate, Sellers et al. used a brain-computer interface (BCI) that relies on spelling. Over the course of a year, the authors tested their BCI in one patient locked-in as a result of a brainstem stroke. The patient would focus on a computer screen with either different choices—Yes, No, Pass, and End—or letters to spell out a desired word. When his desired choice would flash, a concurrent deflection in the brain signal, called the P300 event-related potential, would occur and be recognized by the computer. After calibration of the BCI, the patient was able to freely spell words and relay messages to his wife, such as “Thank you for all of your hard work.” Although this message took 45 min to complete, without such technology communication between the patient and his family would be nearly impossible. The case study by Sellers et al. was in one locked-in patient, and future studies will be needed to determine accuracy and broad applicability. Brain-computer interfaces (BCIs) provide communication that is independent of muscle control, and can be especially important for individuals with severe neuromuscular disease who cannot use standard communication pathways or other assistive technology. It has previously been shown that people with amyotrophic lateral sclerosis (ALS) can successfully use BCI after all other means of independent communication have failed. The BCI literature has asserted that brainstem stroke survivors can also benefit from BCI use. This study used a P300-based event-related potential spelling system. This case study demonstrates that an individual locked-in owing to brainstem stroke was able to use a noninvasive BCI to communicate volitional messages. Over a period of 13 months, the participant was able to successfully operate the system during 40 of 62 recording sessions. He was able to accurately spell words provided by the experimenter and to initiate dialogues with his family. The results broadly suggest that, regardless of the precipitating event, BCI use may be of benefit to those with locked-in syndrome.

Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers

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.

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study

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 (EEG) 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 (SNR) 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 SNR of a users EEG data. We further enhanced the algorithm by incorporating information about the users 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 (DS) (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 DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS 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.

Evaluating Brain-Computer Interface Performance Using Color in the P300 Checkerboard Speller

OBJECTIVE Current Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli. METHODS In addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i.e., Jumpwise). RESULTS Online results (n=36) showed that GC provides higher accuracy and information transfer rate than the CI and GW conditions. Waveform analysis showed that GC produced higher amplitude ERPs than CI and GW. Information transfer rate was improved by the Jumpwise-selected channel locations in all conditions. CONCLUSIONS Unique color stimuli (GC) improved BCI performance and enhanced ERPs. Jumpwise-selected electrode locations improved offline performance. SIGNIFICANCE These results show that in a checkerboard paradigm, unique color stimuli increase BCI performance, are preferred by participants, and are important to the design of end-user applications; thus, could lead to an increase in end-user performance and acceptance of BCI technology.

ALS Population Assessment of a Dynamic Stopping Algorithm Implementation for P300 Spellers

P300-based BCIs have been proposed as potential communication alternatives for individuals whose severe neuro-muscular limitations, e.g. due to amyotrophic lateral sclerosis (ALS), preclude their use of most commercially available assistive technologies. However, BCIs are currently limited by their relatively slower selection rates due to the significant amount of data collection required to improve the signal-to- noise ratio (SNR) of the elicited brain responses to achieve desired system accuracy levels. The conventional strategy is to average over a fixed amount of data prior to BCI decision making, an approach that might be inefficient given the inherent variation in a user’s responses during BCI use. There is need for the development of improved algorithms that can demonstrate increased performance in online testing, especially in target end-user populations. We have developed an algorithm that uses a Bayesian approach to collect only the amount of data necessary to reach a specified confidence level in the BCI’s decision based on continuous evaluation of the quality of a user’s responses. We further optimized the algorithm by incorporating statistical information about the user’s language. Results from online testing in participants with ALS demonstrate that using the Bayesian dynamic stopping algorithm resulted in a significant reduction in character selection time with minimal effect on accuracy, compared to conventional static data collection. In post-use surveys, the participants overwhelmingly preferred the dynamic stopping algorithms.

Evaluating Brain-Computer Interface Performance in an ALS Population: Checkerboard and Color Paradigms

The objective of this study was to investigate the performance of 3 brain-computer interface (BCI) paradigms in an amyotrophic lateral sclerosis (ALS) population (n = 11). Using a repeated-measures design, participants completed 3 BCI conditions: row/column (RCW), checkerboard (CBW), and gray-to-color (CBC). Based on previous studies, it is hypothesized that the CBC and CBW conditions will result in higher accuracy, information transfer rate, waveform amplitude, and user preference over the RCW condition. An offline dynamic stopping simulation will also increase information transfer rate. Higher mean accuracy was observed in the CBC condition (89.7%), followed by the CBW (84.3%) condition, and lowest in the RCW condition (78.7%); however, these differences did not reach statistical significance (P = .062). Eight of the eleven participants preferred the CBC and the remaining three preferred the CBW conditions. The offline dynamic stopping simulation significantly increased information transfer rate (P = .005) and decreased accuracy (P < .000). The findings of this study suggest that color stimuli provide a modest improvement in performance and that participants prefer color stimuli over monochromatic stimuli. Given these findings, BCI paradigms that use color stimuli should be considered for individuals who have ALS.