Common spatial pattern (CSP) is a popular algorithm for spatial filtering and subsequent feature extraction in motor imagery-based (MI) brain-computer interface (BCI) systems. The performance of CSP, however, depends heavily on the subject-specific frequency band and time segment used for classifying mental tasks. Accurate selection of most informative frequency band and time segment poses a great challenge. In this study, quantum particle swarm optimization is proposed for sole selection of frequency band and joint selection of frequency band and time segment, which are realized by a wrapping approach, incorporating CSP for feature extraction and support vector machine for classification into the classification model. The classification error rate is used as the fitness function of quantum particle swarm optimization. The classification performance of quantum particle swarm optimization based CSP algorithm for joint selection of frequency band and time segment is evaluated by comparing with other three CSP algorithms using either fixed frequency band and time segment or fixed time segment and the frequency band selected by particle swarm optimization and quantum-behaved particle swarm optimization, on two MI data sets with different number of channels and trials. Experimental results suggest that the proposed algorithm outperforms the other three algorithms in terms of classification error rate. Across all subjects from the two data sets, the averaged error rate of the proposed algorithm was 7.45%, 2.97% and 2.05% lower than the CSP with fixed frequency band and time segment, that with selected frequency bands by particle swarm optimization and that with selected bands by quantum particle swarm optimization. The proposed algorithm can facilitate the real-world application of BCIs.

中文翻译：

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基于量子粒子群算法的脑机接口频段和时间段组合优化

通用空间模式 (CSP) 是一种流行的算法，用于在基于运动图像 (MI) 的脑机接口 (BCI) 系统中进行空间滤波和后续特征提取。然而，CSP 的性能在很大程度上取决于用于对心理任务进行分类的特定主题的频带和时间段。准确选择信息量最大的频段和时间段是一项巨大的挑战。本研究提出了量子粒子群优化算法用于频带的单独选择和频带与时间段的联合选择，通过包裹的方法实现，将CSP用于特征提取和支持向量机用于分类的分类模型。分类错误率作为量子粒子群优化的适应度函数。通过与其他三种使用固定频带和时间段或固定时间段和粒子群优化选择频带的CSP算法进行比较，评估了基于量子粒子群优化的频带和时间段联合选择CSP算法的分类性能和量子行为的粒子群优化，在两个具有不同数量的通道和试验的 MI 数据集上。实验结果表明，该算法在分类错误率方面优于其他三种算法。在两个数据集的所有受试者中，所提算法的平均错误率分别比固定频段和时间段的 CSP 低 7.45%、2.97% 和 2.05%，通过粒子群优化选择频带和通过量子粒子群优化选择频带。所提出的算法可以促进 BCI 的实际应用。