Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement

Motor imagery-based brain–computer interface (MI-BCI) has been proposed as a rehabilitation tool to facilitate motor recovery in stroke. However, the calibration of a BCI system is a time-consuming and fatiguing process for stroke patients, which leaves reduced time for actual therapeutic interaction. Studies have shown that passive movement (PM) (i.e., the execution of a movement by an external agency without any voluntary motions) and motor imagery (MI) (i.e., the mental rehearsal of a movement without any activation of the muscles) induce similar EEG patterns over the motor cortex. Since performing PM is less fatiguing for the patients, this paper investigates the effectiveness of calibrating MI-BCIs from PM for stroke subjects in terms of classification accuracy. For this purpose, a new adaptive algorithm called filter bank data space adaptation (FB-DSA) is proposed. The FB-DSA algorithm linearly transforms the band-pass-filtered MI data such that the distribution difference between the MI and PM data is minimized. The effectiveness of the proposed algorithm is evaluated by an offline study on data collected from 16 healthy subjects and 6 stroke patients. The results show that the proposed FB-DSA algorithm significantly improved the classification accuracies of the PM and MI calibrated models (p < 0.05). According to the obtained classification accuracies, the PM calibrated models that were adapted using the proposed FB-DSA algorithm outperformed the MI calibrated models by an average of 2.3 and 4.5 % for the healthy and stroke subjects respectively. In addition, our results suggest that the disparity between MI and PM could be stronger in the stroke patients compared to the healthy subjects, and there would be thus an increased need to use the proposed FB-DSA algorithm in BCI-based stroke rehabilitation calibrated from PM

Classification in P2P Networks with Cascade Support Vendor Machines

Classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the trade-off between classification accuracy, model construction, and prediction cost. We provide theoretical insights for the proposed solutions and conduct an extensive set of empirical studies on a number of large-scale datasets. Encouraging results validate the efficacy of the proposed approach.</jats:p

Respiratory viral pathogens among Singapore military servicemen 2009 - 2012: Epidemiology and clinical characteristics

10.1186/1471-2334-14-204BMC Infectious Diseases141-BIDM

P2PDocTagger: Content management through automated P2P collaborative tagging

As the amount of user generated content grows, personal information management has become a challenging problem. Several information management approaches, such as desktop search, document organization and (collaborative) document tagging have been proposed to address this, however they are either inappropriate or inefficient. Automated collaborative document tagging approaches mitigate the problems of manual tagging, but they are usually based on centralized settings which are plagued by problems such as scalability, privacy, etc. To resolve these issues, we present P2PDocTagger, an automated and distributed document tagging system based on classification in P2P networks. P2P-DocTagger minimizes the efforts of individual peers and reduces computation and communication cost while providing high tagging accuracy, and eases of document organization/retrieval. In addition, we provide a realistic and flexible simulation toolkit -- P2PDMT, to facilitate the development and testing of P2P data mining algorithms.</jats:p