An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

Abstract-Nowadays Internet does not provide an exchange of information between applications and networks, which may results in poor application performance. Concepts such as application-aware networking or network-aware application programming try to overcome these limitations. The introduction of Software-Defined Networking (SDN) opens a path towards the realization of an enhanced interaction between networks and applications. SDN is an innovative and programmable networking architecture, representing the direction of the future network evolution. Accurate traffic classification over SDN is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service (QoS) to providing operators with useful forecasts for long-term provisioning. In this paper, four variants of Neural Network estimator are used to categorize traffic by application. The proposed method is evaluated in the four scenarios: feedforward; Multilayer Perceptron (MLP); NARX (LevenbergMarquardt) and NARX (Naïve Bayes). These scenarios respectively provide accuracy of 95.6%, 97%, 97% and 97.6%.