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The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications.

In this paper, enhanced burst-polling dynamic bandwidth allocation (EBDBA) method is proposed to support broadband access networks based on quality of service (QoS) for ethernet passive optical networks (EPONs). EBDBA adaptively increases or decreases the minimum guaranteed bandwidth of the three traffic class-expedited forwarding (EF), assured forwarding (AF), and best effort (BE) trafficaccording to the requested bandwidth of an optical network unit (ONU). Therefore, network resources are efficiently utilized and adaptively allocated to the three traffic classes for unbalanced traffic conditions. Simulation results using OPNET show that EBDBA outperforms conventional bandwidth allocation schemes in terms of the average packet delay (it decreases the maximum performance range to 68%) and the network throughput (it increases maximum performance range to 20%) at a given offered load of 1.2. Index Terms-Dynamic bandwidth allocation (DBA), Ethernet passive optical network (EPON), Quality of service (QoS).