SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge computational complexity for the SHVC encoder. This complexity is mainly because of the the inter/intra-prediction mode search of the coding units. The focus of this study is on developing an efficient complexity reduction for quality scalability of SHVC encoder, with the intention to facilitate the adoption of SHVC for real-time applications. In this regard, first, we build a probabilistic model that uses the mode information and motion homogeneity of already encoded blocks in the enhancement layer (EL) and the base layer to predict the probabilities of all the available inter/intra modes of the to-be-coded block in the EL. Then, we propose an online-learning-based fast mode, assigning (FMA) method that uses the proposed probabilistic model to predict the mode of the to-be-coded block in the EL. Performance evaluation shows that our proposed FMA method reduces the total execution time of the SHVC encoder by 45.40% on average compared with unmodified SHVC codec while maintaining the overall video quality.

An important factor in custom designing a brain computer interface, BCI, is the estimation of the values of its parameters. This paper proposes a fully automatic algorithm that uses Bayesian optimization to tune the hyper-parameters of a synchronous BCI. The algorithm finds a large number of possible sets of values for the hyper-parameters. Each set is then used to train the classifier and the results over the possible sets of hyper-parameter values are aggregated. In this paper we consider a simple motor imagery based BCI with two parameters: the EEG frequency bands and the time intervals from which the features are extracted. We use the linear discriminant analysis classifier and aggregate all results using multi-response linear regression. Experiments using the BCI competition III dataset 3b show that our proposed method results in considerable improvement in the accuracy of a BCI. The average accuracy of our method was 2.6% better than the best results obtained by existing methods.

Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject's brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Brain Computer Interfaces ( BCI) aim at providing a means to control devices with brain signals. Self-paced BCIs, as opposed to synchronous ones, have the advantage of being operational at all times and not only at specific system-defined periods. Traditionally, in the BCI field, a sliding window over the brain signal is used to detect the intention of the user at a given time. This approach ignores the temporal correlations between the adjacent time windows. This paper proposes a novel approach to classify self-paced BCI data using structural support vector machines. Our proposed approach considers the history of the brain signals in the context of sequential supervised learning to better detect the intention of the user from his/her brain signals. We have compared our proposed model to the sliding window approach with Support Vector Machines ( SVM) and Linear Discriminant Analysis ( LDA) classifiers. Using data collected from 4 individuals form BCI competition IV, it is shown that the F-1 score of our approach is significantly better than the sliding window approach. The average F-1 score of our method across all subjects is 0.3 and 0.5 higher than the sliding window with SVM and LDA classifiers, respectively.

A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA) as the classifier of choice for BCI systems.

The new compression standard, known as the High Efficiency Video Coding (HEVC), aims at significantly improving the compression efficiency compared to previous standards. There has been significant interest in developing a scalable version of this standard. As expected, the HEVC scalable video version, which is called SHVC, increases the complexity of the codec compared to the non-scalable counterpart. In this paper, we propose an adaptive fast mode assigning method based on a Bayesian classifier that reduces SHVC's coding complexity by up to 68.55%, while maintaining the overall quality and bit-rates.