Gestures classification is a widespread solution for prosthetic hand control using electromyographic signals (EMG). It can be defined as the task of estimating the class associated to a time-series data provided by the sensor. Nevertheless, it remains unsatisfactory as the number of motions (classes) is limited. In this article, we investigate the problem of estimating the joint coordinates of the hand from the same input data. The main contributions of the article are: (1) a new dataset for joint coordinates estimation from EMG data, (2) a set of neural networks models for this regression task, and (3) an experimental study to compare the performances of the proposed methods. The database is built with a six channels EMG armband and hand motions measured by computer vision. First results are hopeful and provide both a baseline for new methods and a proof of concept for future prosthetic control solutions.

Deep Brain Stimulation (DBS) has proven its efficiency in the treatment of Parkinson’s disease or essential tremor. It requires precise localizations of targets for instance in the thalamus. Since deep brain structures have been shown to be hardly visible on T1 or T2 weighted imaging, most methods rely on atlas based comparison and registration. It is however possible to use direct targeting using a specific MRI sequence called WAIR (White Matter Attenuated Inversion Recovery) even on 1.5 Tesla MRI machine. The direct targeting facilitates the precise segmentation of deep brain structures needed to plan the trajectories of the electrodes for the DBS. But this remains a tedious delineation necessarily done by a neurosurgeon to avoid misinterpretation of the images. In this paper, we propose to build an isotropic super-resolution image for WAIR imaging to facilitate precise direct targeting of anatomical structures in the deep brain. We present a method to perform the reconstruction of a high resolution isotropic WAIR volume from three acquisitions performed on a volunteer subject. The method is based on transfinite interpolation in convex cells of an hyperplane arrangement. Our results show promising quality reconstruction for the computation of a super-resolution WAIR. It allows unambiguous segmentation of the deep brain to be used in DBS surgery.

This paper presents the modeling of a new mini-invasive neurosurgical resection robot. This robot aims to help to remove brain tumors and is incorporated into a multi-robot neurosurgical system. We focus especially on the resection task. The robot is composed of three serial bending modules actuated by wires (cables) and uses an additional translation. The redundancy of the robot (seven degrees of freedom) allows more dexterity for the resection task. A kinematic (geometric) model is built distinguishing the structure and the actuation models, and integrating the influence of the wires into the kinematic behaviour of the robot. A method for kinematic redundancy handling is defined and assigns different parts of the robot to different tasks. An interactive path planning based on arbitrary sequence of elementary procedures assembled by the neurosurgeon is proposed and the yielding procedures are computed from the kinematic model.

Electric chronic stimulation of the human motor cortex (ECSM) has been reported to alleviate chronic severe pain. However the mechanism of action of ECSM is still hypothetical. This is due mainly to the poor knowledge of 1) the electric diffusion through the multiple structures beneath the epidural contacts (i.e. dura matter, cerebrospinal fluid space, arachnoid membrane, grey and white matter layers, pie mere and vascular tree), 2) the absence of consensus concerning the stimulation parameters (mono versus bipolar stimulation, cathodic or anodic current) and 3) the detailed cortical topography of the contacts. In this study we focused on the precise identification of the cortical areas covered by the electric contacts in a series of twelve patients operated on for ECSM. We propose a new automatic tool for topographic analysis able to compute 2D maps from the 3D anatomic MRI with bijective transformation (point- to-point correspondance). Anatomical regions of interest (AROIs) were visually identified, manually outlined and extracted (Iplan, BrainLab, Germany) for further analysis: 1) for the anatomic structures, on pre operative Tl-weigthed magnetic resonance imaging (MRI), the frontal (superior or Fl, intermediate or F2 and inferior or F3), the pre central and the post central gyrus; 2) for the electrode contacts (Resume, Medtronic, USA), on post operative computerized tomography (CT). After getting white and gray matter membership maps by automatic segmentation, we produced a cortical mask to build a triangular mesh. We defined a homeomorphism between the 3D mesh and a subset of R2 and could apply in consequence the circle packing algorithm. We built depth maps (distance to the skull), distance- to-contact maps (distance to a given electrode contact) and anatomic structure maps. Results showed that it was easier to accurately define the location of the contact projection on the cortex allowing physicians to correlate the benefit with the topography. In particular, because of the unfolding, it was easier to integrate the cytoarchitectonics (i.e. the manually identified AROIs) knowledge in the analysis. Beyond the better understanding of ECSM and indirectly of the pathophysiologic process of chronic pain, this new tool might be used in the future for image guided electrode positioning.