Ge Sheng, Wang Ruimin, Yu Dongchuan
Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China.
School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
PLoS One. 2014 Jun 20;9(6):e98019. doi: 10.1371/journal.pone.0098019. eCollection 2014.
With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.
随着脑机接口（BCI）研究的进展，便携式少通道或单通道BCI系统变得很有必要。最近的大多数BCI研究表明，共同空间模式（CSP）算法是提取多类运动想象特征的有力工具。然而，由于CSP算法需要多通道信息，它不适用于少通道或单通道系统。在本研究中，我们应用短时傅里叶变换将单通道脑电图信号分解到时频域并构建多通道信息。使用重建数据，将CSP与支持向量机相结合，以从感觉运动区和前额区的通道获得高分类准确率。这些结果表明，不仅可以从传统的感觉运动区，还可以从前额区通过单通道检测运动想象。
