Motion Imagery Classification Algorithm Research Based on Hybrid Transfer Learning and Application in Brain-computer Interface
DU Yi-hao1,LIU Zhao-jun1,FU Zi-hao1,ZHANG Yuan-yuan1,REN Na2,CHEN Jie3,XIE Ping1
1. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao,Hebei 066004, China
2. Institute of Electrical Engineering, Yanshan University, Qinghuangdao, Hebei 066004, China
3. Institute of Physical Education, Yanshan University, Qinghuangdao, Hebei 066004, China
Abstract To improve the efficiency and universality of transfer learning in the application of motor imagery brain-computer interface (MI-BCI),a hybrid transfer learning model with integrating the advantages of instance transfer and feature transfer learning methods was built.Firstly,the transfer of the instance level by introducing the principle of sample weight polarization to improve the classical TrAdaBoost algorithm was realized,which can optimize training samples in the source domain to some extent.Secondly,to further narrow the distance between the source domain and the target domain,the large margin projected transductive support vector machine was applied to complete the transfer of the feature level,thus maximizing the transfer efficiency.Furthermore,the proposed method was applied to the BCI competition dataset (Dataset IIb data set) for offline test and analysis.The results showed that the hybrid transfer learning model achieved significantly better transfer efficiency than the single transfer learning model,and obtained an improvement of the recognition rate with average value of above 70% for different transfer objects.The results also verified the effectiveness and universality of the hybrid transfer learning model.In addition,the online test was carried out based on the established motor-imagery system,which further verified the practicability of the model.
DU Yi-hao,LIU Zhao-jun,FU Zi-hao, et al. Motion Imagery Classification Algorithm Research Based on Hybrid Transfer Learning and Application in Brain-computer Interface[J]. Acta Metrologica Sinica, 2021, 42(5): 629-637.
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2021, Vol. 42 
