Department of Electronic Engineering, Nanchang University, Nanchang, China.
J Clin Neurophysiol. 2010 Oct;27(5):321-7. doi: 10.1097/WNP.0b013e3181f52f2d.
Feature extractor and classifier are two major components in a brain-computer interface system, in which the feature extractor plays a critical role. To increase the discriminability of features or feature vectors used for classification, it is necessary to select a suitable number of task-related data recording channels. In this article, a machine-learning algorithm is proposed for optimizing feature extraction in an electrocorticogram-based brain-computer interface. Common spatial pattern was used for feature extraction, and channel selection was performed by genetic algorithm for optimizing the feature extraction. Fisher discriminant analysis was used as classifier, and the channel subset chosen at each generation was evaluated by classification accuracy. The algorithm was applied to three electrocorticogram datasets that were recorded during two kinds of motor imagery tasks. The results suggest that the channel number used for building a brain-computer interface system could be significantly decreased without losing classification accuracy, and the accuracy rate could be noticeably improved by using the optimal channel subsets chosen by genetic algorithm.
特征提取器和分类器是脑机接口系统中的两个主要组成部分，其中特征提取器起着关键作用。为了提高用于分类的特征或特征向量的可区分性，有必要选择合适数量的与任务相关的数据记录通道。在本文中，提出了一种用于优化基于脑电图的脑机接口中特征提取的机器学习算法。使用共同空间模式进行特征提取，并通过遗传算法进行通道选择，以优化特征提取。使用 Fisher 判别分析作为分类器，并通过分类准确性来评估每个生成中选择的通道子集。该算法应用于在两种运动想象任务期间记录的三个脑电图数据集。结果表明，在不损失分类准确性的情况下，可以显著减少用于构建脑机接口系统的通道数量，并且通过使用遗传算法选择的最优通道子集，可以显著提高准确性。
