Active Localization and Tracking of Needle and Target in Robotic Image-Guided Intervention Systems.

This paper describes a framework of algorithms for the active localization and tracking of flexible needles and targets during image-guided percutaneous interventions. The needle and target configurations are tracked by Bayesian filters employing models of the needle and target motions and measurements of the current system state obtained from an intra-operative imaging system which is controlled by an entropy-minimizing active localization algorithm. Versions of the system were built using particle and unscented Kalman filters and their performance was measured using both simulations and hardware experiments with real magnetic resonance imaging data of needle insertions into gel phantoms.

Particle Filter Based Active Localization of Target and Needle in Robotic Image-Guided Intervention Systems.

This paper presents a probabilistic method for active localization of needle and targets in robotic image guided interventions. Specifically, an active localization scenario where the system directly controls the imaging system to actively localize the needle and target locations using intra-operative medical imaging (e.g.

Comparison of filtering and classification techniques of electroencephalography for brain-computer interface.

In this paper several methods are investigated for feature extraction and classification of mu features from electroencephalographic (EEG) readings of subjects engaged in motor tasks. EEG features are extracted by autoregressive (AR) filtering, mu-matched filtering, and wavelet decomposition (WD) methods, and the resulting features are classified by a linear classifier whose weights are set by an expert using a-priori knowledge, as well as support vector machines (SVM) using various kernels. The classification accuracies are compared to each other.