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Shojaedini, Seyed Vahab, Morabbi, Sajedeh, Keyvanpour, Mohamad Reza. (1400). A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm. سامانه مدیریت نشریات علمی, 11(3), 357-366. doi: 10.31661/jbpe.v0i0.975
Seyed Vahab Shojaedini; Sajedeh Morabbi; Mohamad Reza Keyvanpour. "A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm". سامانه مدیریت نشریات علمی, 11, 3, 1400, 357-366. doi: 10.31661/jbpe.v0i0.975
Shojaedini, Seyed Vahab, Morabbi, Sajedeh, Keyvanpour, Mohamad Reza. (1400). 'A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm', سامانه مدیریت نشریات علمی, 11(3), pp. 357-366. doi: 10.31661/jbpe.v0i0.975
Shojaedini, Seyed Vahab, Morabbi, Sajedeh, Keyvanpour, Mohamad Reza. A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm. سامانه مدیریت نشریات علمی, 1400; 11(3): 357-366. doi: 10.31661/jbpe.v0i0.975

A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm

Journal of Biomedical Physics and Engineering
مقاله 11، دوره 11، شماره 3، شهریور 2021، صفحه 357-366    اصل مقاله (736.78 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.975
نویسندگان
Seyed Vahab Shojaedini* 1؛ Sajedeh Morabbi2؛ Mohamad Reza Keyvanpour3
1PhD, Associate professor in Biomedical Engineering, Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran
2MSc, Department of Computer Engineering, Alzahra University, Tehran, Iran
3PhD, Associate professor in Computer Engineering, Department of Computer Engineering, Alzahra University, Tehran, Iran
چکیده
Background: Deep neural networks have been widely used in detection of P300 signal in Brain Machine Interface (BMI) systems which are rely on Event-Related Potentials (ERPs) (i.e. P300 signals). Such networks have high curvature variation in their error surface hampering their favorable performance. Therefore, the variations in curvature of the error surface must be minimized to improve the performance of these networks in detecting P300 signals.
Objective: The aim of this paper is to introduce a method for minimizing the curvature of the error surface during training Convolutional Neural Network (CNN). The curvature variation of the error surface is highly dependent on model parameters of deep neural network; therefore, we try to minimize this curvature by optimizing the model parameters.
Material and Methods: In this experimental study an attempt is made to tune the CNN parameters affecting the curvature of its error surface in order to obtain the best possible learning. For achieving this goal, Genetic Algorithm is utilized to optimize the above parameters in order to minimize the curvature variations.
Results: The performance of the proposed algorithm was evaluated on EPFL dataset. The obtained results demonstrated that the proposed method detected the P300 signals with maximally 98.91% classification accuracy and 98.54% True Positive Ratio (TPR).
Conclusion: The obtained results showed that using genetic algorithm for minimizing curvature of the error surface in CNN increased its accuracy in parallel with decreasing the variance of the results. Consequently, it may be concluded that the proposed method has considerable potential to be used as P300 detection module in BMI applications.
کلیدواژه‌ها
Brain-Computer Interfaces؛ Electroencephalogram؛ Neurosciences؛ P300 Signal Detection؛ Curvature Variation؛ Deep learning
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مراجع
  1. Alvarado-Gonzalez M, Garduno E, Bribiesca E, Yanez-Suarez O, Medina-Banuelos V. P300 Detection Based on EEG Shape Features. Comput Math Methods Med. 2016;2016:2029791. doi: 10.1155/2016/2029791. PubMed PMID: 26881010. PubMed PMCID: PMC4736976.
  2. Chowdhury A, Raza H, Meena YK, Dutta A, Prasad G. Online covariate shift detection based adaptive brain-computer interface to trigger hand exoskeleton feedback for neuro-rehabilitation. IEEE Transactions on Cognitive and Developmental Systems. 2017. doi: 10.1109/TCDS.2017.2787040.
  3. Mubeen MA, Knuth KH. Evidence-Based Filters for Signal Detection: Application to Evoked Brain Responses. ArXiv preprint arXiv:11071257. 2011.
  4. Zhang J, Yan C, Gong X, editors. Deep convolutional neural network for decoding motor imagery based brain computer interface. 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC); Xiamen, China: IEEE; 2017. p. 1-5. doi: 10.1109/icspcc.2017.8242581.
  5. Hoffmann U, Vesin JM, Ebrahimi T, Diserens K. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods. 2008;167:115-25. doi: 10.1016/j.jneumeth.2007.03.005. PubMed PMID: 17445904.
  6. Changoluisa V, Varona P, Rodriguez FB, editors. How to Reduce Classification Error in ERP-Based BCI: Maximum Relative Areas as a Feature for P300 Detection. International Work-Conference on Artificial Neural Networks; Springer; 2017. p. 486-97. doi: 10.1007/978-3-319-59147-6_42.
  7. Vareka L, Mautner P, editors. Using the Windowed means paradigm for single trial P300 detection. 38th International Conference on Telecommunications and Signal Processing (TSP); Prague, Czech Republic: IEEE; 2015. p. 1-4. doi: 10.1109/tsp.2015.7296414.
  8. Cecotti H, Graser A. Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans Pattern Anal Mach Intell. 2011;33:433-45. doi: 10.1109/TPAMI.2010.125. PubMed PMID: 20567055.
  9. Lazăr AM, Ursulean R, editors. The P300 event-related potential detection-A morphological approach. E-Health and Bioengineering Conference (EHB); Iasi, Romania: IEEE; 2013. doi: 10.1109/ehb.2013.6707413.
  10. Li Y, Guan C, Li H, Chin Z. A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system. Pattern Recognition Letters. 2008;29:1285-94. doi: 10.1016/j.patrec.2008.01.030.
  11. Sharma N. Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks. ArXiv preprint arXiv:171201977. 2017.
  12. Cecotti H. Toward shift invariant detection of event-related potentials in non-invasive brain-computer interface. Pattern Recognition Letters. 2015;66:127-34. doi: 10.1016/j.patrec.2015.01.015.
  13. Kindermans PJ, Verstraeten D, Schrauwen B. A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI. PLoS One. 2012;7:e33758. doi: 10.1371/journal.pone.0033758. PubMed PMID: 22496763. PubMed PMCID: PMC3319551.
  14. Chen S-W, Lai Y-C. A signal-processing-based technique for P300 evoked potential detection with the applications into automated character recognition. EURASIP J Adv Signal Process. 2014;2014:152. doi: 10.1186/1687-6180-2014-152.
  15. Kottaimalai R, Rajasekaran MP, Selvam V, Kannapiran B, editors. EEG signal classification using principal component analysis with neural network in brain computer interface applications. IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN); Tirunelveli, India: IEEE; 2013. p. 227-31.
  16. Cecotti H, Gräser A. Neural network pruning for feature selection-Application to a P300 Brain-Computer Interface. ESANN. 2009:473-8.
  17. Iyer RK, Jegelka S, Bilmes JA. Curvature and optimal algorithms for learning and minimizing submodular functions. Adv Neural Inf Process Syst. 2013:2742-50.
  18. Dauphin YN, Pascanu R, Gulcehre C, Cho K, Ganguli S, Bengio Y. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Adv Neural Inf Process Syst. 2014:2933-41.
  19. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT press; 2016.
  20. Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng. 2018;15:056013. doi: 10.1088/1741-2552/aace8c. PubMed PMID: 29932424.
  21. Zeiler MD. ADADELTA: an adaptive learning rate method. ArXiv preprint arXiv:12125701. 2012.
  22. Zhang H, Sra S. First-order methods for geodesically convex optimization. Conference on Learning Theory; New York, USA: Columbia University; 2016. p. 1617-38.
  23. Nesterov Y. Introductory lectures on convex optimization: A basic course. New York: Springer Science & Business Media; 2013.
  24. Schaul T, Zhang S, LeCun Y. No more pesky learning rates. International Conference on Machine Learning. 2013:343-51.
  25. Wah BW, Chen Y-X. Constrained genetic algorithms and their applications in nonlinear constrained optimization. Evolutionary Optimization. New York: Springer; 2003. p. 253-75.
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