论文标题
脑电图运动图像上的计算高效的多类时间频率公共空间模式分析
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
论文作者
论文摘要
常见的空间模式（CSP）是一种流行的脑电图（EEG）运动成像（MI）的流行特征提取方法。这项研究修改了常规的CSP算法，以提高多类MI分类精度并确保计算过程有效。 EEG MI数据是从脑部计算机界面（BCI）竞争IV中收集的。首先，对于每个实验试验，进行了带通滤波器和时间频分析。然后，根据CSP特征提取的信号能量选择每个实验试验的最佳EEG信号。最后，提取的特征通过三个分类器，线性判别分析（LDA），幼稚的贝叶斯（NVB）和支持向量机（SVM）进行分类，以进行分类精度比较。实验结果表明，所提出的算法平均计算时间比FBCSP（BCI竞争IV中的第一个冠军）低37.22％，比常规CSP方法长4.98％。对于分类率，与BCI竞赛IV的前三名获胜者相比，拟议的算法KAPPA值获得了第二高。
Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a time-frequency analysis are performed for each experiment trial. Then, the optimal EEG signals for every experiment trials are selected based on the signal energy for CSP feature extraction. In the end, the extracted features are classified by three classifiers, linear discriminant analysis (LDA), naïve Bayes (NVB), and support vector machine (SVM), in parallel for classification accuracy comparison. The experiment results show the proposed algorithm average computation time is 37.22% less than the FBCSP (1st winner in the BCI Competition IV) and 4.98% longer than the conventional CSP method. For the classification rate, the proposed algorithm kappa value achieved 2nd highest compared with the top 3 winners in BCI Competition IV.
