Classification of multi-class motor imagery EEG using four band common spatial pattern.

Brain Computer Interfaces (BCIs) serve as an integration tool between acquired brain signals and external devices. Precise classification of the acquired brain signals with the least misclassification error is an arduous task. Existing techniques for classification of multi-class motor imagery electroencephalogram (EEG) have low accuracy and are computationally inefficient. This paper introduces a classification algorithm, which uses two frequency ranges, mu and beta rythms, for feature extraction using common spatial pattern (CSP) along with support vector machine (SVM) for classification. The technique uses only four frequency bands with no feature reduction and consequently less computational cost. The implementation of this algorithm on BCI competition III dataset IIIa, resulted in the highest classification accuracy in comparison to existing algorithms. A mean accuracy of 85.5 for offline classification has been achieved using this technique.

SmartSIM - a virtual reality simulator for laparoscopy training using a generic physics engine.

Virtual reality (VR) training simulators have started playing a vital role in enhancing surgical skills, such as hand-eye coordination in laparoscopy, and practicing surgical scenarios that cannot be easily created using physical models. We describe a new VR simulator for basic training in laparoscopy, i.e. SmartSIM, which has been developed using a generic open-source physics engine called the simulation open framework architecture (SOFA). This paper describes the systems perspective of SmartSIM including design details of both hardware and software components, while highlighting the critical design decisions. Some of the distinguishing features of SmartSIM include: (i) an easy-to-fabricate custom-built hardware interface; (ii) use of a generic physics engine to facilitate wider accessibility of our work and flexibility in terms of using various graphical modelling algorithms and their implementations; and (iii) an intelligent and smart evaluation mechanism that facilitates unsupervised and independent learning.