Mainsah Boyla O, Morton Kenneth D, Collins Leslie M, Sellers Eric W, Throckmorton Chandra S
IEEE Trans Neural Syst Rehabil Eng. 2015 Sep;23(5):737-43. doi: 10.1109/TNSRE.2014.2374471. Epub 2014 Nov 25.
P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies ( > 70%) to account for error revisions. Error-related potentials (ErrP), which are changes in EEG potentials when a person is aware of or perceives erroneous behavior or feedback, have been proposed as inputs to drive corrective mechanisms that veto erroneous actions by BCI systems. The goal of this study is to demonstrate that training an additional ErrP classifier for a P300 speller is not necessary, as we hypothesize that error information is encoded in the P300 classifier responses used for character selection. We perform offline simulations of P300 spelling to compare ErrP and non-ErrP based corrective algorithms. A simple dictionary correction based on string matching and word frequency significantly improved accuracy (35-185%), in contrast to an ErrP-based method that flagged, deleted and replaced erroneous characters (-47-0%) . Providing additional information about the likelihood of characters to a dictionary-based correction further improves accuracy. Our Bayesian dictionary-based correction algorithm that utilizes P300 classifier confidences performed comparably (44-416%) to an oracle ErrP dictionary-based method that assumed perfect ErrP classification (43-433%).
P300拼写器可为患有严重神经肌肉功能受限的个体提供一种交流方式。然而，要将其用作有效的交流工具，依赖于较高的P300分类准确率（>70%）来处理错误修正。错误相关电位（ErrP）是指当一个人意识到或察觉到错误行为或反馈时脑电图电位的变化，已被提议作为驱动纠正机制的输入，该机制可否决脑机接口系统的错误动作。本研究的目的是证明为P300拼写器训练额外的ErrP分类器并无必要，因为我们假设错误信息已编码在用于字符选择的P300分类器响应中。我们对P300拼写进行离线模拟，以比较基于ErrP和非ErrP的纠正算法。与基于ErrP的标记、删除和替换错误字符的方法（-47-0%）相比，基于字符串匹配和词频的简单字典校正显著提高了准确率（35-185%）。向基于字典的校正提供有关字符可能性的额外信息可进一步提高准确率。我们基于贝叶斯字典的校正算法利用P300分类器的置信度，其表现与基于ErrP字典的神谕方法相当（44-416%），后者假设ErrP分类完美（43-433%）。
