A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

Frontiers in Neuroscience

Guanglin Li¹  Qiong Wang²  Yan Chen³  Wenlong Hang³  Xuejun Liu⁴  Kup-Sze Choi⁵  Jing Qin⁵  Shuang Liang⁶ 

[1] CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China;School of Computer Science and Technology, Nanjing Tech University, Nanjing, China;School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China;Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China;

关键词: motor imagery;    brain-computer interface;    electroencephalography;    support matrix machine;    transfer learning;   

DOI  :  10.3389/fnins.2020.606949

来源: DOAJ

【 摘 要 】

In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.

【 授权许可】

Unknown