Surface Electromyogram (EMG) signals recorded from an amputee's residual muscles have been investigated as a source of control for prosthetic devices for many years. Despite the extensive research focus on the EMG control of arm and gross hand movements, more dexterous individual and combined prosthetic fingers control has not received the same amount of attention. To facilitate such a control scheme, the first and the most significant step is the extraction of a set of highly discriminative feature set that can well separate between the different fingers movements and to do so in a computationally efficient manner. In this paper, an accurate and efficient feature projection method based on Fuzzy Neighborhood Preserving Analysis (FNPA) with QR-decomposition, is proposed and denoted as FNPA. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed FNPA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The method also aims to efficiently overcome the singularity problems of classical LDA and Fuzzy LDA. The proposed FNPA is validated on EMG datasets collected from nine subjects performing 10 classes of individual and combined fingers movements. Practical results indicate the significance of FNPA in comparison to many other feature projection methods with an average accuracy of 91%, using only two EMG electrodes.