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.
杜义浩,刘兆军,付子豪,张园园,任娜,陈杰,谢平. 基于混合迁移学习的运动想象分类算法研究及其在脑机接口中的应用[J]. 计量学报, 2021, 42(5): 629-637.
DU Yi-hao,LIU Zhao-jun,FU Zi-hao,ZHANG Yuan-yuan,REN Na,CHEN Jie,XIE Ping. Motion Imagery Classification Algorithm Research Based on Hybrid Transfer Learning and Application in Brain-computer Interface. Acta Metrologica Sinica, 2021, 42(5): 629-637.
［1］高上凯. 浅谈脑-机接口的发展现状与挑战［J］. 中国生物医学工程学报, 2007, 26 (6): 801-803.
Gao S K. Comments on Recent Progress and Challenges in the Study of Brain-Computer Interface［J］. Chinese Journal of Biomedical Engineering, 2007, 26 (6): 801-803.
［2］张丹, 陈菁菁, 王毅军. 2017年脑机接口研发热点回眸［J］. 科技导报, 2018, 36 (1): 104-109.
Zhang D, Chen J J, Wang Y J. An overeview of the forntier on braincomputer interface technology in 2017［J］. Science & Technology Review, 2018, 36 (1): 104-109.
［3］Millán J D R, Rupp R, Müller-Putz G R, et al. Com-bining Brain-Computer Interfaces and Assistive Techn-ologies: State-of-the-Art and Challenges［J］. Frontiers in Neuroscience, 2010, 4 (5): 161.
［4］Cao X D, Wipf D, Wen F, et al. A Practical Transfer Learning Algorithm for Face Verification［C］//IEEE Computer Society. 2013 IEEE International Conference on Computer Vision (IC-CV). Sydney, NSW, 2013.
［5］胡学钢, 方玉成, 张玉红. 基于Logistic回归分析的直推式迁移学习［J］. 合肥工业大学学报 (自然科学版), 2010, 33 (12): 1797-1801.
Hu X G, Fang Y C, Zhang Y H. Logistic regression for transductive transfer learning［J］. Journal of Hefei Uni-versity of Technology (Natural Science), 2010, 33 (12): 1797-1801.
［6］刘三民, 刘余霞. 基于实例迁移的数据流分类挖掘方法［J］. 信息与控制, 2019, 48 (3): 380-384.
Liu S M, Liu Y X. Classification Mining Method for Data Streams Based on Instances Transfer［J］. Information and Control, 2019, 48 (3): 380-384.
［7］阎高伟, 贺敏, 汤健, 等. 基于最大均值差异多源域迁移学习的湿式球磨机负荷参数软测量［J］. 控制与决策, 2018, 33 (10): 70-75.
Yan G W, He M, Tang J, et al. Soft sensor of wet ball mill load based on maximum mean discrepancy multi-source domain transfer learning［J］. Control and Dec-ision, 2018, 33 (10): 70-75.
［8］韩飞. 基于运动想象的脑电特征提取及特征迁移方法研究［D］. 哈尔滨: 哈尔滨工业大学, 2017.
［9］Pan S J, Kwok J T, Yang Q. Transfer Learning via Dimensionality Reduction［C］// Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, USA, 2008.
［10］Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning［C］// International Conference on Machine Learning, Oregon, USA, 2007.
［11］林程. 基于迁移学习的脑机接口信号分类算法研究［D］. 广州: 华南理工大学, 2017.
［12］Quanz B, Huan J. Large margin transductive transfer learning［C］// Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong, China, 2009.
［13］何群, 杜硕, 王煜文, 等. 基于变分模态分解与深度信念网络的运动想象分类识别研究［J］. 计量学报, 2020, 41 (1): 90-99.
He Q, Du S, Wang Y W, et al. The Classification of EEG Induced by Motor Imagery Based on Variational Mode Decomposition and Deep Belief Network［J］. Acta Metrologica Sinica, 2020, 41 (1): 90-99.
［14］Blankertz B, Klaus-Robert Müller, Krusienski D J, et al. The BCI competition III: Validating alternative appr-oachs to actual BCI problems［J］. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14 (2): 153-159.
［15］Pfurtscheller G. Functional brain imaging based on ERD/ERS［J］. Vision research, 2001, 41 (10-11): 1257.
［16］Pfurtscheller G, Lopes de Silva F H. Event-related EEG/MEG synchronization and desynchronization: basic principles［J］. Clinical Neurophysiology, 1999, 110 (11): 1842-1857.
［17］Hjorth B. EEG analysis based on time domain properties［J］. Electroencephalography & Clinical Neurophysiology, 1970, 29 (3): 0-310.
［18］Oh S H, Lee Y R. A novel eeg feature extraction method using hjorth parameter［J］. International Journal of Electronics and Electrical Engineering, 2014, 2 (2): 106-110.
［19］伍广腾. 基于运动想象的脑-机接口在线异步系统研究［D］. 哈尔滨: 哈尔滨工业大学, 2018.
［20］刘琳琳, 陈健, 李松, 等. 基于相位同步与AR的运动想象脑电信号特征提取研究［J］. 软件导刊, 2018, 17 (3): 7-10.
Liu L L, Chen J, Li S, et al. Feature Extraction of EEG in Motion Imagery Based on Phase Synchronization and AR［J］. Software Guide, 2018, 17 (3): 7-10.
［21］胡春海, 李涛, 刘永红, 等. 基于改进差分进化算法的运动想象脑机接口频带选择［J］. 计量学报, 2018, 39 (2): 134-137.
Hu C H, Li T, Liu Y H, et al. Motor Imagrey Brain Computer Interface Band Selection Based on Improved Differential Evolution Algorithm［J］. Acta Metrologica Sinica, 2018, 39 (2): 276-279.
［22］Gong P, Chen M Y, Zhang L, et al. HHT-Based Selection of Optimal Time-Frequency Patterns for Motor Imagery［J］. Applied Mechanics & Materials, 2012, 380-384 (22): 3522-3525.
［23］谢平, 陈晓玲, 苏玉萍, 等. 基于EMD-多尺度熵和ELM的运动想象脑电特征提取和模式识别［J］. 中国生物医学工程学报, 2013, 32 (6): 641-648.
Xie P, Chen X L, Su Y P, et al. Feature Extraction and Recognition of Motor Imagery EEG Based on EMD-Multiscale Entropy and Extreme Learning Machine［J］. Chinese Journal of Biomedical Engineering, 2013, 32 (6): 641-648.
［24］孟宗, 顾海燕, 刘利晖, 等. 基于EMD与AR谱的轧机主传动系统故障诊断研究［J］. 计量学报, 2011, 32 (4): 338-342.
Meng Z, Gu H Y, Liu L H, et al. Study on Fault Diagnosis of Main Drive System of Rolling Mill Based on EMD and AR Spectrum［J］. Acta Metrologica Sinica, 2011, 32 (4): 338-342.
［25］Dragomiretskiy K, Zosso D. Variational Mode Decompo-sition［J］. IEEE Transactions on Signal Processing, 2014, 62 (3): 531-544.
［26］胡春海, 信思旭, 刘斌, 等. 基于小波变换和盲源分离的P300识别算法研究［J］. 计量学报, 2017, 38 (2): 242-246.
Hu C H, Xin S X, Liu B, et al. Study on Recognition Algorithm of P300 Based on Wavelet Transform and Blind Source Separation［J］. Acta Metrologica Sinica, 2017, 38 (2): 242-246.
［27］李晓竹, 解华. 基于 AdaBoost 的多项式旋流器溢流粒度软测量模型［J］. 计量学报, 2016, 37 (3): 275-278.
Li X Z, Xie H. Polynomial soft sensor model of the overflow particle size of cyclone based on AdaBoost［J］. Acta Metrologica Sinica, 2016, 37 (3): 275-278.
2021, Vol. 42 
