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 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.
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.
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.
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.
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.
In reinforcement learning, when dimensionality of the state space increases, making use of state abstraction seems inevitable. Among the methods proposed to solve this problem, decision tree based ...methods could be useful as they provide automatic state abstraction. But existing methods use univariate, therefore axis-aligned, splits in decision nodes, imposing hyper-rectangular partitioning of the state space. In some applications, multivariate splits can generate smaller and more accurate trees. In this paper, we use oblique decision trees as an instance of multivariate trees to implement state abstraction for reinforcement learning agents. Simulation results on mountain car and puddle world tasks show significant improvement in the average received rewards, average number of steps to finish the task, and size of the trees both in learning and test phases.
Bayesian optimization of BCI parameters Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali
2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
Conference Proceeding
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.
The task of classifying EEG signals for self-paced Brain Computer Interface (BCI) applications is extremely challenging. This difficulty in classification of self-paced data stems from the fact that ...the system has no clue about the start time of a control task and the data contains a large number of periods during which the user has no intention to control the BCI. Therefore, to improve the performance of the BCI, it is imperative to exploit the characteristics of the EEG data as much as possible. For motor imagery based self-paced BCIs, during motor imagery task the EEG signal of each subject goes through several internal state changes. Applying appropriate classifiers that can exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an algorithm which is able to capture the temporal correlation of the EEG signal. We compare the performance of our algorithm that is based on neural network conditional random fields to two well-known dynamic classifiers, the Hidden Markov Models and Conditional Random Fields and to the static classifier, Support Vector Machines. We compare these methods using the data from SM2 dataset, and we show that our algorithm yields results that are considerably superior to the other approaches in terms of the Area Under the Curve (AUC) of the BCI system.
Much attention has been directed towards synchronous Brain Computer Interfaces (BCIs). For these BCIs, the user can only operate the system during specific system-defined periods. Self-paced BCIs, ...however, allow users to operate the system at any time he/she wishes. The classification of Electroencephalography (EEG) signals in self-paced BCIs is extremely challenging, as the BCI system does not have any clue about the start time of a control task. Also, the data contains a large number of periods during which the user has no intention to control the BCI. ;
For sensory motor self-paced BCIs (focus of this thesis), the brain of a user goes through several well-defined internal state changes while performing a mental task. Designing classifiers that exploit such temporal correlations in EEG data can enhance the performance of BCIs. It is also important to customize these BCIs for each user, because the brain characteristics of different people are not the same. ;
In this thesis, we first develop a unified comparison framework to compare the performance of different classifiers in sensory motor BCIs followed by rigorous statistical tests. This study is the largest of its kind as it has been performed on 29 subjects of synchronous and self-paced BCIs. We then develop a Bayesian optimization-based strategy that automatically customizes a synchronous BCI based on the brain characteristics of each individual subject. Our results show that our automated algorithm (which relies on less sophisticated feature extraction and classification methods) yields similar or superior results compared to the best performing designs in the literature.;
We then propose an algorithm that can capture the time dynamics of the EEG signal for self-paced BCI systems. We show that this algorithm yields better results compared to several well-known algorithms, over 13 self-paced BCI subjects. Finally, we propose a fully automatic, scalable algorithm that customizes a self-paced BCI system based on the brain characteristics of each user and at the same time captures the dynamics of the EEG signal. Our final algorithm is an important step towards transitioning BCIs from research environments to real-life applications, where automatic, scalable and easy to use systems are needed.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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 F1 score of our approach is significantly better than the sliding window approach. The average F1 score of our method across all subjects is 0.3 and 0.5 higher than the sliding window with SVM and LDA classifiers, respectively.
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