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

Abstract

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.

Subject-Independent P300 BCI Using Ensemble Classifier, Dynamic Stopping and Adaptive Learning

“…Brain-computer interfaces (BCIs) are used to translate intentions of users, especially those with severe motor impairments such as amyotrophic lateral sclerosis (ALS) [1], [2], into the system's commands. BCIs primarily use electroencephalograms (EEG), which is the electrical measurements on the human scalp, as the main modality.…”

“…To deal with this trade-off, various dynamic stopping (DS) methods had been proposed [1], [10], [18]- [22] to abruptly stop the flashing paradigm at any moment, when a set of user-defined conditions is satisfied. DS methods therefore can significantly reduce the spelling time while also preserving the expected accuracy that could be achieved by static stopping methods.…”

“…Mainsah [1], [10], Clements [21], Kindermans [11], [22] all used the similar DS method on different signal processing frameworks. They implement the DS by updating the posterior probabilities of each letter via a Bayesian interference scheme after each iteration, given the classifier score history of the previous iterations.…”

Sequential Detection of P300 Waves for High-Throughput Brain-Computer Interfaces

“…Thus, recent studies have focused on reducing the number of repeated trials for detecting a P300 wave while maintaining the accuracy of BCIs [13][14][15]. In [13], stimulus sets were adaptively switched depending on some performance criterion.…”

“…Introducing such flexibility to the BCI system could potentially decrease the latency, but selecting the optimal criterion remained elusive. In [14,15], a Bayesian approach was adopted by continuously updating probabilities of target characters. Such a probabilistic model offered an optimal control of the number of repetitions.…”