Yin Xuxian, Xu Baolei, Jiang Changhao, Fu Yunfa, Wang Zhidong, Li Hongyi, Shi Gang
State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, People's Republic of China. University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
J Neural Eng. 2015 Jun;12(3):036004. doi: 10.1088/1741-2560/12/3/036004. Epub 2015 Apr 2.
In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching.
The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs).
In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG-fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature.
Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.
为了增加脑机接口（BCI）分类的状态数量，我们采用了一种运动想象任务，让受试者想象手部握拳的力量和速度。
该BCI同时利用记录的脑电图（EEG）和功能近红外光谱（fNIRS）信号。从EEG中提取时相频率特征，而血红蛋白差（HbD）[氧合血红蛋白（HbO）与脱氧血红蛋白（Hb）的差值]特征用于提高fNIRS的分类准确率。利用联合互信息（JMI）特征选择准则对EEG和fNIRS特征进行组合和优化；然后使用极限学习机（ELM）对提取的特征进行分类。
在本研究中，时相频率特征实现的EEG信号平均分类准确率比单一类型特征提高了7%，达到18%，比共同空间模式（CSP）特征提高了15%。fNIRS信号的HbD特征比Hb、HbO或总血红蛋白（HbT）的准确率提高了1%，达到4%。用于解码手部握拳力量和速度的运动想象的EEG-fNIRS特征准确率达到89%±2%，比单独的EEG或fNIRS特征准确率提高了1%至5%。
我们新颖的运动想象范式通过增加提取的命令数量提高了BCI性能。时相频率特征和HbD特征分别提高了EEG和fNIRS信号的分类准确率，并且混合EEG-fNIRS技术在两类运动想象中实现了更高的解码准确率，这可能为未来的多模态在线BCI系统提供框架。
