A Robust PCA-SURE Thresholding Deep Neural Network Approach for Mental Task Brain Computer Interface
DOI:
https://doi.org/10.26713/jims.v11i3-4.900Keywords:
Brain computer interface, Principal component analysis, Deep learning, EEG and SURE thresholdingAbstract
Electroencephalographic (EEG) characteristics, i.e., non-linear structure and nonstationarity, make mental state recognition not a trivial task to various classification models. In this paper, a combined principal component analysis (PCA) – Deep neural network approach is proposed as a robust and effective solution to classifying EEG signals recorded with low-cost and portable recording systems into different mental states towards implementation of a Brain computer interface (BCI) capable of controlling electronic devices. Stein's unbiased risk estimate (SURE) thresholding – PCA is utilized to obtain spectral features that are most essential for deep neural network classifier to perform at best. The contributions of this paper are three–fold. First, we propose a novel, robust and efficient method that integrates SURE thresholding, PCA and Deep Neural Network (DNN), along with other signal processing and machine learning techniques for mental state recognition. Second, a complete mental-task-based BCI using an appropriate experimental design without any assistant equipment is presented. Third, SURE risk thresholding is utilized and proven to be an effective method to automatically determine the appropriate number of principal components of EEG features returned by performing PCA. Experimental results show that our method outperforms others in an EEG dataset of four subjects with highest classification accuracies on dual and triple mental state task experiments of 96.83% and 76.90%, respectively.
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References
H. Abdi and L.J. Williams, Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics 2(4) (2010), 433 – 459, DOI: 10.1002/wics.101.
D.J. Abson, A.J. Dougill and L.C. Stringer, Using principal component analysis for information-rich socio-ecological vulnerability mapping in Southern Africa, Applied Geography 35(1) (2012), 515 – 524, DOI: 10.1016/j.apgeog.2012.08.004.
S. Amiri, R. Fazel-Rezai and V. Asadpour, A review of hybrid brain-computer interface systems, Advances in Human-Computer Interaction 2013 (2013), 1, DOI: 10.1155/2013/187024.
C.W. Anderson, E.A. Stolz and S. Shamsunder, Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Transactions on Biomedical Engineering 45(3) (1998), 277 – 286, DOI: 10.1109/10.661153.
N.T. Anh, T.H. Hoang, V.T. Thang and T.Q. Bui, An Artificial Neural Network approach for electroencephalographic signal classification towards brain-computer interface implementation, in Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016 IEEE RIVF International Conference, November 7, 2016, pp. 205 – 210, DOI: 10.1109/RIVF.2016.7800295.
F. Beverina, G. Palmas, S. Silvoni, F. Piccione and S. Giove, User adaptive BCIs: SSVEP and P300 based interfaces, PsychNology Journal 1(4) (2003), 331 – 354, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.3731&rep=rep1&type=pdf.
B. Blankertz, K.R. Muller, D.J. Krusienski, G. Schalk, J.R. Wolpaw, A. Schlogl, G. Pfurtscheller, J.R. Millan, M. Schroder and N. Birbaumer, The BCI competition III: Validating alternative approaches to actual BCI problems, IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2) (2006), 153 – 159, DOI: 10.1109/TNSRE.2006.875642.
E.M. Braack, B. de Jonge, M.J. van Putten, Reduction of TMS induced artifacts in EEG using principal component analysis, IEEE Transactions on Neural Systems and Rehabilitation Engineering 21(3) (2013), 376 – 382, DOI: 10.1109/TNSRE.2012.2228674.
S.G. Chang, B. Yu and M. Vetterli, Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing 9(9) (2000), 1532 – 1546, DOI: 10.1109/83.862633.
S. Chiappa and D. Barber, EEG classification using generative independent component analysis, Neurocomputing 69(7) (2006), 769 – 777, DOI: 10.1016/j.neucom.2005.12.028.
D.L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory 41(3) (1995), 613 – 627, DOI: 10.1109/18.382009.
D.L. Donoho and I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association 90(432) (1995), 1200 – 1224, URL: https://www.jstor.org/stable/2291512.
R. Fazel-Rezai, B.Z. Allison, C. Guger, E.W. Sellers, S.C. Kleih and A. Kübler, P300 brain computer interface: current challenges and emerging trends,Frontiers in Neuroengineering 17(5) (2012), 14, DOI: 10.3389/fneng.2012.00014.
C. Guger, S. Daban, E. Sellers, C. Holzner, G. Krausz, R. Carabalona, F. Gramatica and G. Edlinger, How many people are able to control a P300-based brain–computer interface (BCI)?, Neuroscience Letters 462(1) (2009), 94 – 98.
C. Guger, Using a brain/computer interface for smart-home control, PerAdaMagazine Toward Pervasive Adaptation (2009 July), DOI: 10.1016/j.neulet.2009.06.045.
M. Hajinoroozi, Z. Mao, T.P. Jung, C.T. Lin and Y. Huang, EEG-based prediction of driver's cognitive performance by deep convolutional neural network, Signal Processing: Image Communication 47 (2016), 549 – 555, DOI: 10.1016/j.image.2016.05.018.
G.E. Hinton and R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313(5786) (2006), 504 – 507, DOI: 10.1126/science.1127647.
G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets, Neural Computation 18(7) (2006), 1527 – 1554, DOI: 10.1162/neco.2006.18.7.1527.
M.R. Hohmann, T. Fomina, V. Jayaram, N. Widmann, C. Förster, J. Just, M. Synofzik, B. Schölkopf, L. Schöls and M. Grosse-Wentrup, A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis, Progress in Brain Research 228 (2016), 221 – 239, DOI: 10.1016/bs.pbr.2016.04.022.
I.T. Jolliffe and J. Cadima, Principal component analysis: a review and recent developments, Phil. Trans. R. Soc. A 374(2065) (2016), 20150202, DOI: 10.1098/rsta.2015.0202.
J.P. Jung, S. Makeig, C. Humphries, T.W. Lee, M.J. Mckeown, V. Iragui and T.J. Sejnowski, Removing electroencephalographic artifacts by blind source separation, Psychophysiology 37(2) (2000), 163 – 178, DOI: 10.1111/1469-8986.3720163.
I. Käthner, A. Kübler and S. Halder, Rapid P300 brain-computer interface communication with a head-mounted display, Frontiers in Neuroscience 9 (2015), 207, DOI: 10.3389/fnins.2015.00207.
S.P. Kelly, E.C. Lalor, R.B. Reilly and J.J. Foxe, Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication, IEEE Transactions on Neural Systems and Rehabilitation Engineering 13(2) (2005), 172 – 178, DOI: 10.1109/TNSRE.2005.847369.
L. Khedher, J. Ramírez, J.M. Górriz, A. Brahim and F. Segovia, Alzheimer's Disease Neuroimaging Initiative, Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images, Neurocomputing 151 (2015), 139 – 150, DOI: 10.1007/978-3-319-18914-7_9.
V. Krishnaveni, S. Jayaraman, L. Anitha and K. Ramadoss, Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients, Journal of Neural Engineering 3(4) (2006), 338, DOI: 10.1088/1741-2560/3/4/011.
N.S. Kwak, K.R. Müller and S.W. Lee, A lower limb exoskeleton control system based on steady state visual evoked potentials, Journal of Neural Engineering 12(5) (2015), 056009, DOI: 10.1088/1741-2560/12/5/056009.
E.C. Lalor, S.P. Kelly, C. Finucane, R. Burke, R. Smith, R.B. Reilly and G. Mcdarby, Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment, EURASIP Journal on Applied Signal Processing 2005 (2005), 3156 – 3164, DOI: 10.1155/ASP.2005.3156.
Y. LeCun, Y. Bengio and G. Hinton, Deep learning, Nature 521(7553) (2015), 436 – 444, DOI: 10.1038/nature14539.
R. Leeb, S. Perdikis, L. Tonin, A. Biasiucci, M. Tavella, M. Creatura, A. Molina, A. Al-Khodairy, T. Carlson and J.D. Millán, Transferring brain–computer interfaces beyond the laboratory: successful application control for motor-disabled users, Artificial Intelligence in Medicine 31(59) (2013), 121 – 132, DOI: 10.1016/j.artmed.2013.08.004.
C.T. Lin, B.S. Lin, F.C. Lin and C.J. Chang, Brain computer interface-based smart living environmental auto-adjustment control system in UPnP home networking, IEEE Systems Journal 8(2) (2014), 363 – 370, DOI: 10.1109/JSYST.2012.2192756.
Z. Lin, C. Zhang, W. Wu and X. Gao, Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs, IEEE Transactions on Biomedical Engineering 54(6) (2007), 1172 – 1176, DOI: 10.1109/TBME.2006.889197.
F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche and B. Arnaldi, A review of classification algorithms for EEG-based brain–computer interfaces, Journal of Neural Engineering 4(2) (2007), R1, DOI: 10.1088/1741-2560/4/2/R01.
N. Lu, T. Li, X. Ren and H. Miao, A deep learning scheme for motor imagery classification based on restricted boltzmann machines, IEEE Transactions on Neural Systems and Rehabilitation Engineering August 17, 2016, DOI: 10.1109/TNSRE.2016.2601240.
G.R. Müller-Putz, R. Scherer, C. Brauneis and G. Pfurtscheller, Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components, Journal of Neural Engineering 2(4) (2005), 123, DOI: 10.1088/1741-2560/2/4/008.
D.J. McFarland, D.J. Krusienski, W.A. Sarnacki and J.R. Wolpaw, Emulation of computer mouse control with a noninvasive brain-computer interface, Journal of Neural Engineering 5(2) (2008), 101, DOI: 10.1088/1741-2560/5/2/001.
M. Middendorf, G. McMillan, G. Calhoun and K.S. Jones, Brain-computer interfaces based on the steady-state visual-evoked response, IEEE Transactions on Rehabilitation Engineering 8(2) (2000), 211 – 214, URL: https://www.ncbi.nlm.nih.gov/pubmed/10896190.
J.D. Millán, F. Renkens, J. Mouriño and W. Gerstner, Brain-actuated interaction, Artificial Intelligence 159(1-2) (2004), 241 – 259, DOI: 10.1016/j.artint.2004.05.008.
J. Minguillon, M.A. Lopez-Gordo and F. Pelayo, Trends in EEG-BCI for daily-life: Requirements for artifact removal, Biomedical Signal Processing and Control 31 (2017), 407 – 418, DOI: 10.1016/j.bspc.2016.09.005.
G.R. Muller-Putz and G. Pfurtscheller, Control of an electrical prosthesis with an SSVEPbased BCI, IEEE Transactions on Biomedical Engineering 55(1) (2008), 361 – 364, DOI: 10.1109/TBME.2007.897815.
H.A. Nguyen, J. Musson, F. Li, W. Wang, G. Zhang, R. Xu, C. Richey, T. Schnell, F.D. McKenzie and J. Li, EOG artifact removal using a wavelet neural network, Neurocomputing 97 (2012), 374 – 389, DOI: 10.1016/j.neucom.2012.04.016.
A. Nijholt, D. Tan, B. Allison, J. del R. Milan and B. Graimann, Brain-computer interfaces for HCI and games, in CHI'08 Extended Abstracts on Human Factors in Computing Systems, pp. 3925 – 3928 (2008), DOI: 10.1145/1358628.1358958.
S.M. Park, T.J. Lee and K.B. Sim, Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform, International Journal of Control, Automation and Systems 14(6) (2016), 1582 – 1587, DOI: 10.1007/s12555-016-0031-9.
R.D. Pascual-Marqui, Discrete, 3D distributed, linear imaging methods of electric neuronal activity, Part 1: exact, zero error localization, arXiv preprint https://arxiv.org/abs/0710.3341, October 17, 2007.
R.D. Pascual-Marqui, Review of methods for solving the EEG inverse problem, International Journal of Bioelectromagnetism 1(1) (1999), 75 – 86, URL: http://www.brainmaster.com/software/pubs/brain/loreta/TechnicalDetails.pdf.
S. Pyatykh, J. Hesser and L. Zheng, Image noise level estimation by principal component analysis, IEEE Transactions on Image Processing 22 (2013), 687 – 699, DOI: 10.1109/TIP.2012.2221728.
B. Rebsamen, E. Burdet, C. Guan, H. Zhang, C.L. Teo, Q. Zeng, C. Laugier, M.H. Ang Jr. Controlling a wheelchair indoors using thought, IEEE Intelligent Systems 22(2) (2007), DOI: 10.1109/MIS.2007.26.
S. Saeedi, R. Chavarriaga and J.D. Millán, Long-term stable control of motor-imagery BCI by a locked-in user through adaptive assistance, IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(4) (2017), 380 – 391, DOI: 10.1109/TNSRE.2016.2645681.
R. Salakhutdinov and G. Hinton, Deep boltzmann machines, in Artificial Intelligence and Statistics, 448 – 455 (2009), URL: http://proceedings.mlr.press/v5/salakhutdinov09a/salakhutdinov09a.pdf.
G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer and J.R. Wolpaw, BCI2000: a generalpurpose brain-computer interface (BCI) system, IEEE Transactions on Biomedical Engineering 51(6) (2004), 1034 – 1043, DOI: 10.1109/TBME.2004.827072.
L.C. Shi, R.N. Duan and B.L. Lu, A robust principal component analysis algorithm for EEG-based vigilance estimation, in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, July 3, 2013, pp. 6623 – 6626, DOI: 10.1109/EMBC.2013.6611074.
S. Siuly and Y. Li, Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification, Computer Methods and Programs in Biomedicine 119(1) (2015), 29 – 42, DOI: 10.1016/j.cmpb.2015.01.002.
C.M. Stein, Estimation of the mean of a multivariate normal distribution, The Annals of Statistics 1 (1981), 1135 – 1151, URL: https://projecteuclid.org/euclid.aos/1176345632.
Y.R. Tabar and U. Halici, A novel deep learning approach for classification of EEG motor imagery signals, Journal of Neural Engineering 14(1) (2016), 016003, DOI: 10.1088/1741-2560/14/1/016003.
J.A. Urigüen and B. Garcia-Zapirain, EEG artifact removal”state-of-the-art and guidelines, Journal of Neural Engineering 12(3) (2015), 031001, DOI: 10.1088/1741-2560/12/3/031001.
A. Vallabhaneni, T. Wang and B. He, Brain-computer interface, in: Neural Engineering 2005 (pp. 85-121), Springer, US, Optoelectronics & Advanced Materials 8, 1377 (2006), DOI: 10.1007/0-306-48610-5_3.
J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson and T.M. Vaughan, Brain-computer interface technology: a review of the first international meeting, IEEE Transactions on Rehabilitation Engineering 8(2) (2000), 164 – 173, https://www.ncbi.nlm.nih.gov/pubmed/10896178.
F.Z. Yetkin, R.A. Papke, L.P. Mark, D.L. Daniels, W.M. Mueller and V.M. Haughton, Location of the sensorimotor cortex: functional and conventional MR compared, American Journal of Neuroradiology 16(10) (1995), 2109 – 2113, DOI: https://www.ncbi.nlm.nih.gov/pubmed/8585502.
M.H. Zarifia, N.K. Ghalehjogh and M. Baradaran-nia, A new evolutionary approach for neural spike detection based on genetic algorithm, Expert Systems with Applications 42(1) (2015), 462 – 467, DOI: 10.1016/j.eswa.2014.07.038.
T. Zeyl, E. Yin, M. Keightley and T. Chau, Adding real-time Bayesian ranks to error-related potential scores improves error detection and auto-correction in a P300 speller, IEEE Transactions on Neural Systems and Rehabilitation Engineering 24(1) (2016), 46 – 56, DOI: 10.1109/TNSRE.2015.2461495.
X.P. Zhang and M.D. Desai, Adaptive denoising based on SURE risk, IEEE Signal Processing Letters 5(10) (1998), 265 – 267, DOI: 10.1109/97.720560.
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