Abstract Aiming at nonlinear and non-stationary characteristics of motor imagery(MI) based electroencephalogram, a features extraction and patterns recognition algorithm is proposed. Firstly, overlapped filter bank(OFB) pre-processing is conducted. Then, the common spatial patterns(CSP) algorithm is applied to the filtered electroencephalogram(EEG) signals. Afterwards, the OFB-CSP features are incorporated into robust support matrix machine(RSMM) for MI patterns recognition, and the corrected particle swarm optimization(CPSO) algorithm is utilized to dynamically adjust the optimal parameters for RSMM classification. Experiments on two public datasets show that OFB pre-processing improves the discrimination of CSP features. Besides, the optimal parameters for EEG signals of individuals are searched by CSPO to the RSMM classifier. Compared with the state-of-the-arts algorithms, the proposed algorithm significantly improves MI classification accuracy. With less requirements of samples and computational resources, the proposed overlapped features strategy and parameters optimization algorithm is suitable for real-world brain computer interface application.
Fund:Supported by National Natural Science Foundation of China(No.61673322), Fujian Young and Middle-Aged Teacher Education Research Project(No.JAT190067)
Corresponding Authors:
LUO Tianjian, Ph.D., lecturer. His research interests include pattern recognition and brain computer interface.
About author:: ZHOU Changle, Ph.D., professor. His research interests include artificial intelligent and pattern recognition.
LUO Tianjian,ZHOU Changle. Overlapped Features Strategy and Parameters Optimization Patterns Recognition for Motor Imagery EEG[J]. , 2020, 33(8): 692-704.
[1] LEBEDEV M A, NICOLELIS M A L. Brain-Machine Interfaces: Past, Present and Future. Trends in Neurosciences, 2006, 29(9): 536-546. [2] 吴朝晖,俞一鹏,潘 纲,等.脑机融合系统综述.生命科学, 2014, 26(6): 645-649. (WU Z H, YU Y P, PAN G, et al. Brain-Machine Integrated Systems. Life Sciences, 2014, 26(6): 645-649.) [3] LEBEDEV M. Brain-Machine Interfaces: An Overview. Translational Neuroscience, 2014, 5(1): 99-110. [4] DONCHIN E, SPENCER K M, WIJESINGHE R. The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 174-179. [5] MÜLLER-PUTZ G R, SCHERER R, BRAUNEIS C, et al. Steady-State Visual Evoked Potential(SSVEP)-Based Communication: Impact of Harmonic Frequency Components. Journal of Neural Engineering, 2005, 2(4): 123-130. [6] PFURTSCHELLER G, DA SILVA F H L. Event-Related EEG/MEG Synchronization and Desynchronization: Basic Principles. Clinical Neurophysiology, 1999, 110(11): 1842-1857. [7] PFURTSCHELLER G, NEUPER C. Future Prospects of ERD/ERS in the Context of Brain-Computer Interface(BCI) Developments. Progress in Brain Research, 2006, 159: 433-437. [8] BLANKERTZ B, TOMIOKA R, LEMM S, et al. Optimizing Spatial Filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine, 2008, 25(1): 41-56. [9] JAMALOO F, MIKAEILI M. Discriminative Common Spatial Pattern Sub-bands Weighting Based on Distinction Sensitive Learning Vector Quantization Method in Motor Imagery Based Brain-Computer Interface. Journal of Medical Signals and Sensors, 2015, 5(3): 156-161. [10] ANG K K, CHIN Z Y, WANG C C, et al. Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b. Frontiers in Neuroscience, 2012, 6. DOI: 10.3389/fnins.2012.00039. [11] LEMM S, BLANKERTZ B, CURIO G, et al. Spatio-Spectral Filters for Improving the Classification of Single Trial EEG. IEEE Transactions on Biomedical Engineering, 2005, 52(9): 1541-1548. [12] DORNHEGE G, BLANKERTZ B, KRAULEDAT M, et al. Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing. IEEE Transactions on Biomedical Engineering, 2006, 53(11): 2274-2281. [13] LOTTE F, GUAN C T. Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms. IEEE Transactions on Biomedical Engineering, 2011, 58(2): 355-362. [14] WU W, CHEN Z, GAO X R, et al. Probabilistic Common Spatial Patterns for Multichannel EEG Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 639-653. [15] ZHANG Y, WANG Y, JIN J, et al. Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification. International Journal of Neural Systems, 2017, 27(2). DOI: 10.1142/S0129065716500325. [16] TANGERMANN M, MÜLLER K R, AERTSEN A, et al. Review of the BCI Competition IV. Frontiers in Neuroscience, 2012, 6. DOI: 10.3389/fnins.2012.00055. [17] TABAR Y R, HALICI U. A Novel Deep Learning Approach for Classification of EEG Motor Imagery Signals. Journal of Neural Engineering, 2016, 14(1). DOI: 10.1088/1741-2560/14/1/016003. [18] 唐智川,张克俊,李 超,等.基于深度卷积神经网络的运动想象分类及其在脑控外骨骼中的应用.计算机学报, 2017, 40(6): 1367-1378. (TANG Z C, ZHANG K J, LI C, et al. Motor Imagery Classification Based on Deep Convolutional Neural Network and Its Application in Exoskeleton Controlled by EEG. Journal of Computers, 2017, 40(6): 1367-1378.) [19] SCHIRRMEISTER R T, SPRINGENBERG J T, FIEDERER L D, J, et al. Deep Learning with Convolutional Neural Networks for EEG Decoding and Visualization. Human Brain Mapping, 2017, 38(11): 5391-5420. [20] AMIN S, ALSULAIMAN M, MUHAMMAD G, et al. Deep Lear-ning for EEG Motor Imagery Classification Based on Multi-layer CNNs Feature Fusion. Future Generation Computer Systems, 2019, 101: 542-554. [21] MANDHANA V, TAWARE R. Classification of 3D Interpolated EEG Signals Using Hybrid R-3DCNN // Proc of the 35th Annual ACM Symposium on Applied Computing. New York, USA: ACM, 2020: 17-19. [22] LUO T J, ZHOU C L, CHAO F. Exploring Spatial-Frequency-Sequential Relationships for Motor Imagery Classification with Recu-rrent Neural Network. BMC Bioinformatics, 2018, 19(1). DOI: 10.1186/s12859-018-2365-1. [23] LIN C T, CHEN Y C, HUANG T Y, et al. Development of Wireless Brain Computer Interface with Embedded Multitask Scheduling and Its Application on Real-Time Driver′s Drowsiness Detection and Warning. IEEE Transactions on Biomedical Engineering, 2008, 55(5): 1582-1591. [24] LUO L, XIE Y B, ZHANG Z H, et al. Support Matrix Machines // Proc of the International Conference on Machine Learning. Berlin, Germany: Springer, 2015: 938-947. [25] ZHENG Q Q, ZHU F Y, HENG P A. Robust Support Matrix Machine for Single Trial EEG Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(3): 551-562. [26] MOSAVI M R, AYATOLLAHI A, AFRAKHTEH S. An Efficient Method for Classifying Motor Imagery Using CPSO-Trained ANFIS Prediction. Evolving Systems, 2019. DOI: 10.1007/s12530-019-09280-x. [27] LU H P, ENG H L, GUAN C T, et al. Regularized Common Spatial Pattern with Aggregation for EEG Classification in Small-Sample Setting. IEEE Transactions on Biomedical Engineering, 2010, 57(12): 2936-2946. [28] GUO X C, YANG J H, WU C G, et al. A Novel LS-SVMs Hyper- Parameter Selection Based on Particle Swarm Optimization. Neurocomputing, 2008, 71(16/17/18): 3211-3215. [29] HSU W Y. Single-Trial Motor Imagery Classification Using Asy-mmetry Ratio, Phase Relation, Wavelet-Based Fractal, and Their Selected Combination. International Journal of Neural Systems, 2013, 23(2): DOI: 10.1142/S012906571350007X. [30] LI D, ZHANG H X, KHAN M S, et al. A Self-adaptive Frequency Selection Common Spatial Pattern and Least Squares Twin Support Vector Machine for Motor Imagery Electroencephalography Recognition. Biomedical Signal Processing and Control, 2018, 41: 222-232. [31] LI Y, ZHANG Y N, CHENG Y L, et al. A Novel Immune Quantum-Inspired Genetic Algorithm // Proc of the International Confe-rence on Natural Computation. Berlin, Germany: Springer, 2005: 215-218.
2020, Vol. 33 
