Sofien Gannouni

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase and amplitude of the brain signals using fast Fourier transform (FFT) and autoregressive (AR) modeling of the reconstructed phase space as well as the modification of the BCI parameters (trial length, trial frequency band, classification method). We report interesting results compared with those present in the literature by utilizing sequential forward floating selection (SFFS) and a multi-class linear discriminant analysis (LDA), our findings showed superior classification results, a classification accuracy of 86.06% and 93% for two BCI competition datasets, with respect to results from previous studies.

Web service composition: Models and approaches

The use of Web services as a standard technology facilitates seamless business-to-business (B2B) interaction and the process of integrating various systems and applications. It enables mass knowledge distribution by providing a standard way of exposing data sources and applications/systems as Web services. These services can be created/generated, updated, and composed at runtime because they are loosely coupled. Web service composition provides the ability to compose services by manually or automatically generating a service composition plan in order to achieve the business goal, resolve a scientific issue/problem or provide new service functionality. They are reusable services that can be used to implement a business process. Web service composition can be achieved via two models: dynamic and static. This paper introduces taxonomy of Web service composition models and approaches, and provides a survey of Web service composition models and their approaches. Additionally, we present comparisons of different models and approaches for each model.

BCWB: A P300 Brain-Controlled Web Browser

Web access and web resources open many horizons, their usage increases in all life aspects including government, education, commerce and entertainment, where the key to such resources lies in Web browsers. Acknowledging the importance of universal accessibility to web resources, the W3C has developed a series of guidelines into a Web Accessibility Initiative (WAI), with the goal of providing access to web resources for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, the authors believe that the development of novel assistive technologies using new human-computer interfaces will go a long way towards achieving this lofty goal. In this paper, they present a P300 Electroencephalography Brain-controlled Web browser to enhance the accessibility of people with severe motor disabilities to Web resources. It enhances their interaction with the Web taking their needs into account. The proposed Web browser satisfies the Mankoffs requirements of a system that would “allow true web access.â€

AFIRM: Adaptive forwarding based link recovery for mobility support in NDN/IoT networks

Abstract The Internet of Things (IoT) ecosystem includes a plethora of devices equipped with heterogeneous communication interfaces. They exhibit different mobility patterns and hardware constraints as memory, battery and processing power. On the other hand, IoT applications are overlayed on top of these constrained-devices imposing stringent requirements in term of data availability, data coherence, and response latency. To cope with these challenges, Named Data Networking (NDN) architecture is positioned in the middle layer to act as the networking layer. By providing easy data access thanks to the unique and location-independent content names, in-network caching and name-based routing, NDN expects to hide from IoT applications the complexity and diversity of the underlying Things by adapting the network operation to their features. In this paper, we focus on data availability requirements threatened by high IoT network dynamics related to sensors mobility. We address the producer mobility issue in NDN/IoT networks using the routing-based approach. We propose a novel and efficient forwarding algorithm named AFIRM in order to support producer mobility, and compare its performances to those of other relevant solutions based on the routing approach.

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities when controlling home devices. However, the development of such EEG-based control system requires a great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach integrates adaptive filtering techniques customized for each subject during the training phase to increase the accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data sets provided by BCI-competition and then using our own on-line validation platform experienced with four subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and I² bands. These features are classified by the Linear Discriminant Analysis (LDA) algorithm. Our prototype integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we integrated all EEG signal processing including the adaptive filtering. The proposed system achieves 80.5% of classification accuracy, which makes approach a promising method to control an external devices based on the thought of LH and RH movement.

An embedded implementation based on adaptive filter bank for brain-computer interface systems

BACKGROUND Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. NEW-METHOD This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. RESULTS The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. COMPARISON-WITH-EXISTING-METHOD Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. CONCLUSIONS Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates.

A Gamma-calculus GPU-based parallel programming framework

The General Purpose GPU computational model changes the way parallel processing can be achieved. It is becoming more attractive to carry out parallel tasks on GPU devices. The sequential part of the application runs on the CPU whereas the computationally-intensive part is accelerated by the GPU. GPUs provide a multithreaded high level of parallelism with hundreds of cores. For high performance computing developers, the GPU cores offer a higher magnitude order of raw computation power than CPU. In this paper we propose an efficient parallel programming framework based on the GPU devices. This framework adopts the Gamma formalism as an abstract model for making parallelism less difficult. The software developer has only to specify the action to be curried-out on any atomic portion of data. The framework will then run the given action simultaneously on the GPU cores.

Data integration using service composition in data service middleware

Service-oriented computing has emerged as the state-of-the-art distributed computing model for loosely coupled service-centric business applications. Specifically, service-oriented computing techniques allow organizations to compose Web services. Indeed, the organizational capacity to compose Web services has received much interest, particularly with respect to supporting business-to-business or enterprise application integration. In this paper, we present a system that proposes to adopt service composition as a new approach to integrate data from various data sources. We specify the business process description language as well as the service composition engine that supports the proposed language. We also present different strategies for executing a composite data service call. Copyright

Semantic Web Service Composition Standards and their Applications

Web services allow developers to create, generate and compose them at runtime. However, a single web service is not sufficient to achieve most of user demands in its own. This gives rise to the concept of web services composition of is an appropriate solution to maximize the benefits of web services. Web services composition has received a great attention from different communities. A number of different semantic standards/specifications have been proposed to tackle this issue. These standards are Ontology Web Language and Web Service Modeling Ontology. In this paper, we provide an overview of these standards and present a comparison between them. We also overview different applications that have developed based on each of these standards and present comparisons among them.

Data sharing in distributed computing environment

The evolution of distributed computing has made sharing of data sources an urgent necessity at the present time. It is desired to benefit from distributed data sources that spread across a network. Efforts have been made by researchers and private companies to propose approaches for accessing remote, heterogeneous and autonomous data sources to share them across a network. Other efforts are looking at these approaches and the concepts in them with the aim of developing applications in both centralized and decentralized environments to provide access to and sharing of data sources. In this paper, we review two data sharing approaches that have been proposed, namely Transaction Processing Monitors and Resource Description Framework. For each approach, we will present its architecture, limitations and problems, as well as applications that have been developed based on its concepts.