Li Lili, Xu Guanghua, Zhang Feng, Xie Jun, Li Min
School of Mechanical Engineering, Xi'an Jiaotong UniversityXi'an, China.
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong UniversityXi'an, China.
Front Neurosci. 2017 Jun 29;11:371. doi: 10.3389/fnins.2017.00371. eCollection 2017.
Brain computer interfaces provide a novel channel for the communication between brain and output devices. The effectiveness of the brain computer interface is based on the classification accuracy of single trial brain signals. The common spatial pattern (CSP) algorithm is believed to be an effective algorithm for the classification of single trial brain signals. As the amplitude feature for spatial projection applied by this algorithm is based on a broad frequency bandpass filter (mainly 5-30 Hz) in which the frequency band is often selected by experience, the CSP is sensitive to noise and the influence of other irrelevant information in the selected broad frequency band. In this paper, to improve the CSP, a novel relevant feature integration and extraction algorithm is proposed. Before projecting, we integrated the motor relevant information to suppress the interference of noise and irrelevant information, as well as to improve the spatial difference for projection. The algorithm was evaluated with public datasets. It showed significantly better classification performance with single trial electroencephalography (EEG) data, increasing by 6.8% compared with the CSP.
脑机接口为大脑与输出设备之间的通信提供了一种全新的渠道。脑机接口的有效性基于单次试验脑信号的分类准确率。共同空间模式（CSP）算法被认为是一种用于单次试验脑信号分类的有效算法。由于该算法应用的空间投影幅度特征基于一个宽频带通滤波器（主要是5 - 30赫兹），其中频带通常是凭经验选择的，所以CSP对噪声以及所选宽频带中其他无关信息的影响很敏感。在本文中，为了改进CSP，提出了一种新颖的相关特征整合与提取算法。在进行投影之前，我们整合了运动相关信息，以抑制噪声和无关信息的干扰，同时提高投影的空间差异。该算法使用公开数据集进行了评估。结果表明，对于单次试验脑电图（EEG）数据，其分类性能显著更好，与CSP相比提高了6.8%。
