DOI: 10.14704/nq.2018.16.6.1666

EEG Classification Based on Sparse Representation and Deep Learning

Guangchun Gao, Lina Shang, Kai Xiong, Jian Fang, Cui Zhang, Xuejun Gu


For brain computer interfaces (BCIs) research, the classification of motor imagery brain signals is a major and challenging step. Based on the traditional sparse representation classification, a classification algorithm of electroencephalogram (EEG) based on sparse representation and convolution neural network is proposed by this paper. For the EEG signal, firstly, the features of the signal are obtained through the common spatial pattern (CSP) algorithm, and then the redundant dictionary with sparse representation is constructed based on these features. The sparse representation of the EEG signal is completed and the sparse features can be obtained. Finally, the sparse features are transformed into two dimensional signals, and the convolution neural network is used to complete the classification of EEG signals. Using the dataset downloaded from the website of BCI competition III (dataset IVa), for two types of EEG signals, the experiments show that the recognition accuracy of the method is over 80, and the recognition accuracy is better than that of the traditional SRC algorithm.


Deep Learning, Sparse Representation Classification, EEG, Convolution Neural Network

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An X, Kuang D, Guo X, Zhao Y, He L. A Deep Learning Method for Classification of EEG Data Based on Motor Imagery. Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014: 203-10.

Argunsah AO, Cetin M. AR-PCA-HMM Approach for Sensor motor Task Classification in EEG-Based Brain Computer Interfaces. Proceedings of the 20th International Conference on Pattern Recognition 2010: 113-16.

Bashashati H, Ward HK, Bashashati A. Bayesian optimization of BCI parameters. 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 2016: 1-5.

Blankertz B, Tomioka R, Lemm S; Kawanabe M, Muller KR. Optimizing Spatial filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine 2008; 25(1): 41-56.

Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 2006: 489-590.

Candes E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate information. Communications on Pure and Applied Mathematics 2005; 59:1207-33.

Cecotti H, Gr¨aser A. Convolutional neural networks for P300 detection with applicationto brain-computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011; 33(3): 433-45.

Deriche M, Alani A. A new algorithm for EEG feature selection using mutual information. Acoustics, Speech, and Signal Processing Proceedings 2001:1057-60.

Donoho D. Compressed sensing. IEEE Trans. Inform. Theory 2006; 1289-1306.

Gao XZ, Wang J, Tanskanen JMA, Bie R, Guo P. BP Neural Networks with Harmony Search Method-based Training for Epileptic EEG Signal Classification. 2012 Eighth International Conference on Computational Intelligence and Security 2012: 252-57.

Goker I, Osman I, Ozekes S. Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms. Journal of Medical Systems 2012; 36(5): 2705-11.

Harikumar Rajaguru; Sunil Kumar Prabhakar, Epilepsy classification using fuzzy optimization and Kernel Fisher discriminant analysis, 2017 2nd International Conference on Communication and Electronics Systems (ICCES) 2017: 183-86.

Hortal E, Planelles D, Costa A, Iáñez E, Úbeda A, Azorín JM, Fernández E. SVM-based Brain–Machine Interface for controlling a robot arm through four mental tasks. Neurocomputing 2015; 151: 116–21.

Kaper M, Meinicke P, Grossekathoefer U, Lingner T, Ritter H. BCI competition 2003-data set IIb: Support vector machines for the P300 speller paradigm. IEEE Trans. on Biomedical Engineering 2004; 51(6): 1073-76.

Kapoor E, Johnson V, Pati S, Chakka VK. Fourier decomposition method-based descriptor of EEG signals to identify dementia. 2016 IEEE Region 10 Conference (TENCON) 2016; 2474-78.

Krahenbuhl P, Doersch C, Donahue J, Darrell T. Data-dependent initializations of convolutional neural networks. Computer Science 2015; 11(1): 1-12.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 2012; 60(2): 1097–1105.

Lu N, Li T, Ren X, Miao H. A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Transactions on Neural Systems And Rehabilitation Engineering 2017; 25(6): 566-76.

Lv JM, Luo JQ, Yuan XH. Application of chaos analytic methods based on normal EEG. 2004 3rd International Conference on Computational Electromagnetics and Its Applications 2004: 426-29.

Mishuhina V, Jiang XD. Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI. IEEE Signal Processing Letters 2018; 25(6): 783-87.

Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications 2009; 36(2): 2027-36.

Ramoser H, Muller-Gerking J, Pfurtscheller G, Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 2000; 8(4): 441-46.

Roeva O, Atanassova V. Cuckoo search algorithm for model parameter identification. International Journal Bioautomation 2016; 20(4): 483-92

Seth D, Chakraborty D, Ghosal P, Sanyal SK. Brain computer interfacing: A spectrum estimation based neurophysiological signal interpretation. 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) 2017: 534-39.

Shin Y, Lee S, Ahn M, Cho H, Jun SC, Lee HN. Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications. Computers in Biology and Medicine 2015; 66(11): 29-38.

Subasi A, Gursoy MI. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications 2010; 37(12): 8659-66.

Thornton KE. Electrophysiological(QEEG) correlates of effective reading: towards a generator/ activation theory of the mind. Journal of Neurotherapy 2002; 6(3): 37-66.

Wang Y, Gao S, Gao X. Common spatial pattern method for channel selelction in motor imagery-based brain-computer interface. 27th Annual International Conference of the Engineering in Medicine and Biology Society IEEE-EMBS 2005: 5392-95.

Weis M, Jannek D, Roemer F, Guenther T, Haardt M, Husar P. Multi-dimensional PARAFAC2 component analysis of multi-channel EEG data including temporal tracking. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2010: 5375-78.

Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain–computer interfaces for communication and control. Clinical Neurophysiology 2002; 113(6): 767-91.

Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009; 31(2): 210-27.

Yang Y, Yu ZL, Gu ZH. A New Method for Motor Imagery Classification Based on Hidden Markov Model. Proceedings of the 7th IEEE Conference on Industrial Electronics and Applications 2012: 1588-91.

Yong X, Ward RK, Birch GE. Sparse spatial filter optimization for EEG channel reduction in brain- computer interface. IEEE International Conference on Acoustics, Speech and Signal Processing 2008; 417–20.

Zhong MJ, Lotte F, Girolami M, Lécuyer A. Classifying EEG for brain computer interfaces using Gaussian processes. Pattern Recognition Letters 2008; 29(3): 354-59.

Zhou W, Yang Y, Yu Z. Discriminative dictionary learning for EEG signal classification in Brain-computer interface. 12th International Conference on Control Automation Robotics & Vision (ICARCV) 2012: 1582–85.

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