Higger Matt, Quivira Fernando, Akcakaya Murat, Moghadamfalahi Mohammad, Nezamfar Hooman, Cetin Mujdat, Erdogmus Deniz
IEEE Trans Neural Syst Rehabil Eng. 2017 Jun;25(6):704-714. doi: 10.1109/TNSRE.2016.2590959. Epub 2016 Jul 13.
Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g., language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g., P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP "Shuffle" Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier's mistakes across a particular user's SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.
脑机接口（BCIs）试图从可分类的生理状态即脑信号中推断出某些任务符号，即与任务相关的指令。例如，在运动想象机器人控制任务中，用户会通过从较小的脑信号字典（想象左手或右手运动）中进行选择，从任务符号字典（向左转动手臂、抓握等）中表明自己的选择。我们研究了脑机接口如何利用脑信号选择来推断任务符号。我们提供了一个递归贝叶斯决策框架，该框架纳入了上下文先验分布（例如拼写应用中的语言模型先验），考虑了不同脑信号的准确性，并且对单个脑信号查询错误具有鲁棒性。这个框架与最大互信息（MMI）编码相结合，MMI编码使信息传输率（ITR）的一种推广最大化。两者都适用于任何离散任务和脑现象（例如P300、稳态视觉诱发电位（SSVEP）、运动想象（MI））。为了证明我们方法的有效性，我们进行了稳态视觉诱发电位“洗牌”拼写器实验，并将我们的递归编码方案与包括霍夫曼编码在内的传统决策树方法进行比较。MMI编码利用了特定用户稳态视觉诱发电位响应中分类器错误的不对称性；这样做虽然在我们的实验中速度慢了13%，但字母准确率提高了33%。
