Humans use their hands mainly for grasping and manipulating objects, performing simple and dexterous tasks. The loss of a hand may significantly affect one's working status and independence in daily life. A restoration of the grasping ability is important to improve the quality of the daily life of the patients with motion disorders. Although neuroprosthetic devices restore partially the lost functionality, the user acceptance is low, possibly due to the artificial and unnatural operation of the devices. This thesis addresses this problem in reach-to-grasp motions with the development of shared control approaches that enable a seamless and more natural operation of hand prosthesis. In the first part, we focus on the identification of the grasping intention during the reach-to-grasp motion with able-bodied individuals. We propose an Electromyographic (EMG)-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. In this approach, the utilization of Echo State Networks encloses efficiently the dynamics of the muscle activation enabling a fast identification of the grasp type in real-time. We also examine the impact of different object distance and speed on the detection time and accuracy of the classifier. Although the distance from the object has no significant effect, fast motions influence significantly the performance. In the second part, we evaluate and extend our approach on four real end-users, i.e. individuals with below the elbow amputation. For addressing the variability of the EMG signals, we separate the reach-to-grasp motion into three phases, with respect to the arm extension. A multivariate analysis of variance on the muscle activity reveals significant differences among the motion phases. Additionally, we examine the classification performance on these phases and compare the performance of different pattern recognition methods. An on-line evaluation with an upper-limb prosthesis shows that the inclusion of the reaching motion in the training of the classifier improves importantly classification accuracy. In the last part of the thesis, we explore further the concept of motion phases on the EMG signals and its potentials on addressing the variability of the signals. We model the dynamic muscle contractions of each class with Gaussian distributions over the different phases of the overall motion. We extend our previous analysis providing insights on the LDA projection and quantifying the similarity of the distributions of the classes (i.e grasp types) with the Hellinger distance. We notice larger values of the Helinger distance and, thus, smaller overlaps among the classes with the segmentation to motion phases. A Linear Discriminant Analysis classifier with phase segmentation affects positively the classification accuracy.