Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.
Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.
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DOI：
10.1088/1741-2560/12/1/016013
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发表时间：
2015-02
影响因子：
4
通讯作者：
Throckmorton CS
中科院分区：
文献类型：
--
作者：
Mainsah BO;Collins LM;Colwell KA;Sellers EW;Ryan DB;Caves K;Throckmorton CS
P300拼写器是一种脑机接口（BCI），通过利用脑电图数据中诱发的大脑信号，有可能恢复患有严重神经肌肉残疾（如肌萎缩侧索硬化症（ALS））的患者的沟通能力。然而，由于需要在多次试验中对数据进行平均，以提高所激发的大脑信号的信噪比，因此使用bci进行准确拼写的速度很慢。动态控制数据收集的概率方法在非残疾人群中显示出更好的性能；然而，这些方法尚未在目标脑机接口用户群体中得到验证。我们开发了一种基于贝叶斯推理的P300拼字器数据驱动算法，该算法根据用户脑电图数据的急性信噪比自适应选择试验次数，从而提高了拼写时间。我们通过结合用户的语言信息进一步增强了算法。在当前的研究中，我们通过比较动态停止（或早期停止）算法与当前最先进的静态数据收集方法的性能，在目标BCI用户群体中在线测试和验证算法，静态数据收集方法在在线操作之前收集的数据量是固定的。ALS患者动态停止算法的在线测试结果表明，在保持选择准确性的同时，以比特/秒（100-300%）和理论比特率（100-550%）为单位的通信速率显着增加。参与者也压倒性地喜欢动态停止算法。我们已经开发了一种可行的脑机接口算法，该算法已经在目标脑机接口人群中进行了测试，该算法具有翻译潜力，可以提高脑机接口拼写者的表现，从而更实际地用于交流。
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. 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. 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. 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.