中文摘要：

脑电信号的时间和频率间隔选择对脑机接口的分类性能具有重要的影响。针对多类运动想象脑机接口系统，提出一个新的基于时间段和频带联合选择的分类算法。该算法首先使用滑动窗将运动想象产生的脑电信号在时域和频域进行分割，然后在每一对截取的时间段和频段，使用多类共空域模式算法提取脑电特征信号，最后使用k－最近邻算法对特征信号进行分类。交叉验证的分类识别率作为最优时间段和频带的选择标准。使用一个四类数据集对这个分类算法的性能进行了测试。与现有的3个典型算法比较，这个算法取得了最高的平均分类正确率，证实了这个基于时间段和频带联合选择的分类算法的有效性。

英文摘要：

The time and frequency intervals of EEG signals have significant influence on the classification performance of brain-computer interfaces（BCIs）.Based on joint selection of time segment and frequency band,a new classification algorithm was proposed to classify multiclass motor imagery BCI systems in this paper.A sliding window was firstly used to segment the EEG signals in time and frequency domain.Then the multiclass common spatial pattern（CSP）was utilized for feature extraction in each pair of time and frequency segment.Finally we employed the k-nearest neighbor（KNN）to classify the extracted feature signals.Cross-validated classification accuracy was used for the selection criterion of optimal time and frequency segment.The performance of the algorithm was tested and compared with those of three typical algorithms by a classification experiment on a four-class data set.The results suggested that the algorithm could achieve a highest accuracy rate,validating the effectiveness of the j oint selection of time segment and frequency band based classification algorithm.