A New Method to Improve the Performance of Deep Neural Networks in Detecting P۳۰۰ Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm

Background: Deep neural networks have been widely used in detection of P۳۰۰ signal in Brain Machine Interface (BMI) systems which are rely on Event-Related Potentials (ERPs) (i.e. P۳۰۰ 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 P۳۰۰ 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 P۳۰۰ signals with maximally ۹۸.۹۱% classification accuracy and ۹۸.۵۴% 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 P۳۰۰ detection module in BMI applications.

Keywords:

Brain-Computer Interfaces , Electroencephalogram , Neurosciences , P۳۰۰ Signal Detection , Curvature Variation , Deep Learning