Mainsah B O, Collins L M, Colwell K A, Sellers E W, Ryan D B, Caves K, Throckmorton C S
Duke University, Department of Electrical and Computer Engineering, USA.
J Neural Eng. 2015 Feb;12(1):016013. doi: 10.1088/1741-2560/12/1/016013. Epub 2015 Jan 14.
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.
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 user's EEG 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 (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.
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.
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.
P300 拼写器是一种脑机接口（BCI），它可以通过利用脑电图（EEG）数据中诱发的脑信号，为患有严重神经肌肉残疾的个体（如肌萎缩侧索硬化症（ALS）患者）恢复沟通能力。然而，由于需要对多个试验的数据进行平均以提高诱发脑信号的信噪比（SNR），使用脑机接口进行准确拼写的速度较慢。动态控制数据收集的概率方法在非残疾人群中已显示出更好的性能；然而，尚未在目标脑机接口用户群体中对这些方法进行验证。
我们基于贝叶斯推理开发了一种用于 P300 拼写器的数据驱动算法，该算法通过根据用户 EEG 数据的即时 SNR 自适应选择试验次数来提高拼写速度。我们通过纳入有关用户语言的信息进一步增强了该算法。在本研究中，我们通过将动态停止（DS）（或早期停止）算法的性能与当前最先进的方法——静态数据收集（即在在线操作之前收集的数据量是固定的）进行比较，在目标脑机接口用户群体中对这些算法进行在线测试和验证。
对 ALS 患者进行的 DS 算法在线测试结果表明，以比特/分钟衡量的通信速率（提高了 100 - 300%）和理论比特率（提高了 100 - 550%）显著增加，同时保持了选择准确性。参与者也压倒性地更喜欢 DS 算法。
我们开发了一种可行的脑机接口算法，该算法已在目标脑机接口人群中进行了测试，具有转化潜力，可提高脑机接口拼写器的性能，使其更实际地用于通信。
