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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، مرداد و 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
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