EEG recognition of natural hand movements based on transfer learning
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TP181;TN911.7;R318

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    Abstract:

    In the field of Brain-Computer Interface (BCI),the recognition of natural hand movements through electroencephalography (EEG) is crucial for achieving natural and precise human-machine interaction.However,attempts to enhance model generalization ability across different subjects using transfer learning are still rare in studies focusing on natural hand movement paradigms.Here,we investigate three natural hand movement paradigms of grasping,pinching and twisting through EEG experiments,and validate the effectiveness of two transfer learning algorithms,namely CA-MDM(Covariance matrix centroid Alignment-Minimum Distance to Riemannian Mean) and CA-JDA(Covariance matrix centroid Alignment-Joint Distribution Adaptation),on our experimental dataset.The results show that CA-JDA achieves average accuracies of 60.51%±5.78% and 34.89%±4.42% in binary and quadruple classification tasks,respectively,while CA-MDM performs at 63.88%±4.59% and 35.71%±4.84% in the same tasks,highlighting the advantages of Riemannian space-based classifiers in handling covariance features.This study not only confirms the feasibility of transfer learning in natural hand movement paradigms but also aids in reducing calibration time for BCI systems and implementing natural human-machine interaction strategies.

    Reference
    [1] He Y T,Eguren D,Azorín J M,et al.Brain-machine interfaces for controlling lower-limb powered robotic systems[J].Journal of Neural Engineering,2018,15(2):021004
    [2] Galán F,Nuttin M,Lew E,et al.A brain-actuated wheelchair:asynchronous and non-invasive brain-computer interfaces for continuous control of robots[J].Clinical Neurophysiology,2008,119(9):2159-2169
    [3] Müller-Putz G R,Scherer R,Pfurtscheller G,et al.Brain-computer interfaces for control of neuroprostheses:from synchronous to asynchronous mode of operation[J].Biomedizinische Technik Biomedical Engineering,2006,51(2):57-63
    [4] Pfurtscheller G,Guger C,Müller G,et al.Brain oscillations control hand orthosis in a tetraplegic[J].Neuroscience Letters,2000,292(3):211-214
    [5] Slobounov S M,Ray W J.Movement-related potentials with reference to isometric force output in discrete and repetitive tasks[J].Experimental Brain Research,1998,123(4):461-473
    [6] Jochumsen M,Rovsing C,Rovsing H,et al.Quantification of movement-related EEG correlates associated with motor training:a study on movement-related cortical potentials and sensorimotor rhythms[J].Frontiers in Human Neuroscience,2017,11:604
    [7] Ahmadian P,Sanei S,Ascari L,et al.Constrained blind source extraction of readiness potentials from EEG[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering:a Publication of the IEEE Engineering in Medicine and Biology Society,2013,21(4):567-575
    [8] Jochumsen M,Rovsing C,Rovsing H,et al.Classification of hand grasp kinetics and types using movement-related cortical potentials and EEG rhythms[J].Computational Intelligence and Neuroscience,2017,2017:7470864
    [9] Schwarz A,Ofner P,Pereira J,et al.Decoding natural reach-and-grasp actions from human EEG[J].Journal of Neural Engineering,2018,15(1):016005
    [10] Schwarz A,Pereira J,Kobler R,et al.Unimanual and bimanual reach-and-grasp actions can be decoded from human EEG[J].IEEE Transactions on Bio-Medical Engineering,2020,67(6):1684-1695
    [11] Wang J R,Bi L Z,Fei W J,et al.Decoding single-hand and both-hand movement directions from noninvasive neural signals[J].IEEE Transactions on Bio-Medical Engineering,2021,68(6):1932-1940
    [12] Chen Y F,Fu R Q,Wu J D,et al.Continuous bimanual trajectory decoding of coordinated movement from EEG signals[J].IEEE Journal of Biomedical and Health Informatics,2022,26(12):6012-6023
    [13] Pan S J,Yang Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359
    [14] Jayaram V,Alamgir M,Altun Y,et al.Transfer learning in brain-computer interfaces[J].IEEE Computational Intelligence Magazine,2016,11(1):20-31
    [15] Jin Y M,Mousavi M,Sa V R D.Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces[C]//2018 6th International Conference on Brain-Computer Interface (BCI).January 15-17,2018,Gangwon,Korea (South).IEEE,2018:1-4
    [16] Wu D R,Lance B J,Parsons T D.Collaborative filtering for brain-computer interaction using transfer learning and active class selection[J].PLoS One,2013,8(2):e56624
    [17] Gao Y Y,Liu Y C,She Q S,et al.Domain adaptive algorithm based on multi-manifold embedded distributed alignment for brain-computer interfaces[J].IEEE Journal of Biomedical and Health Informatics,2023,27(1):296-307
    [18] Zhang W,Wu D R.Manifold embedded knowledge transfer for brain-computer interfaces[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2020,28(5):1117-1127
    [19] She Q S,Cai Y H,Du S Z,et al.Multi-source manifold feature transfer learning with domain selection for brain-computer interfaces[J].Neurocomputing,2022,514:313-327
    [20] Zanini P,Congedo M,Jutten C,et al.Transfer learning:a Riemannian geometry framework with applications to brain-computer interfaces[J].IEEE Transactions on Bio-Medical Engineering,2018,65(5):1107-1116
    [21] Congedo M,Barachant A,Bhatia R.Riemannian geometry for EEG-based brain-computer interfaces:a primer and a review[J].Brain-Computer Interfaces,2017,4(3):155-174
    [22] Yger F,Berar M,Lotte F.Riemannian approaches in brain-computer interfaces:a review[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2017,25(10):1753-1762
    [23] Arsigny V,Fillard P,Pennec X,et al.Fast and simple calculus on tensors in the log-Euclidean framework[M]//Lecture Notes in Computer Science.Berlin,Heidelberg:Springer Berlin Heidelberg,2005:115-122
    [24] Pan S J,Tsang I W,Kwok J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210
    [25] 毋雪雁,王水花,张煜东.K最近邻算法理论与应用综述[J].计算机工程与应用,2017,53(21):1-7 WU Xueyan,WANG Shuihua,ZHANG Yudong.Survey on theory and application of k-nearest-neighbors algorithm[J].Computer Engineering and Applications,2017,53(21):1-7
    [26] Mueller-Putz G R,Scherer R,Brunner C,et al.Better than random:a closer look on BCI results[J].International Journal of Bioelectromagnetism,2008,10(1):52-55
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XUE Muhui, XU Baoguo, LI Lang, SONG Aiguo. EEG recognition of natural hand movements based on transfer learning[J]. Journal of Nanjing University of Information Science & Technology,2025,17(2):245-255

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History
  • Received:May 12,2024
  • Online: April 16,2025
  • Published: March 28,2025
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