Huang Zhihua, Li Minghong, Ma Yuanye
College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China.
Department of Physiology, Yunnan University of Traditional Chinese Medicine, Kunming, China.
Comput Math Methods Med. 2018 Oct 2;2018:4089021. doi: 10.1155/2018/4089021. eCollection 2018.
This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.
这项工作旨在提高单个脑电图时段的分类准确率，减少重复刺激的次数，并提高P300拼写器的信息传输率（ITR）。目标脑电图时段和非目标脑电图时段都通过小波映射到一个空间中。在这个空间中，使用Fisher准则来衡量目标和非目标之间的差异。仅选择少数对应于较大差异的Daubechies小波基来构建一个矩阵，通过该矩阵将脑电图时段转换为特征向量。为确保在线实验，计算任务被分配到由Storm管理和集成的多台计算机上，以便它们能够并行执行。通过测试其对单个脑电图时段进行分类和检测字符的性能，将所提出的特征提取方法与典型方法进行了比较。我们的方法实现了更高的分类和检测准确率。ITR也反映了我们方法的优越性。我们方法的并行计算方案部署在一个由三台台式计算机组成的小规模Storm集群上。一轮脑电图时段的平均反馈时间为1.57毫秒。所提出的方法可以提高P300拼写器脑机接口的性能。其并行计算方案能够支持在线实验所需的快速反馈。我们的方法可以显著减少重复刺激的次数。并行计算方案不仅支持我们的小波特征提取，还为为P300拼写器开发的其他算法提供了一个框架。
