A Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information. During EEG acquisition, artifacts are induced due to involuntary eye movements or eye blink, casting adverse effects on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy. One of the designed SAE models outperforms the performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study, may provide a contribution towards the state of the art performance on the problem of EEG based eye state classification.

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The H.264/AVC video coding standard uses in intraprediction, 9 directional modes for 4 × 4 and 8 × 8 luma blocks, and 4 directional modes for 16 × 16 luma macroblocks, and 8 × 8 chroma blocks. The use of the variable block size and multiple modes in intraprediction makes the intracoding of H.264/AVC very efficient compared with other compression standards; however, computational complexity is increased significantly. In this paper, we propose a fast mode selection algorithm for intracoding. This algorithm is based on the vector of the block's gravity center whose direction is used to select the best candidate prediction mode for intracoding. On this basis, only a small number of intraprediction modes are chosen for rate distortion optimization (RDO) calculation. Different video sequences are used to test the performance of proposed method. The simulation results show that the proposed algorithm increases significantly the speed of intracoding with negligible loss of peak signal-to-noise ratio quality.

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