Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG) and Impedance Cardiography (ICG) are among physiological signals widely used in various biomedical applications including health tracking, sleep quality assessment, early disease detection/diagnosis and human affective state recognition. This paper presents the development of a biosignal-specific processing and feature extraction tool for analyzing these physiological signals according to the state-of-the-art studies reported in the scientific literature. This tool is intended to assist researchers in machine learning and pattern recognition to extract feature matrix from these bio-signals automatically and reliably. In this paper, we provided the algorithms used for the signal-specific filtering and segmentation as well as extracting features that have been shown highly relevant to a better category discrimination in an intended application. This tool is an open-source software written in MATLAB and made compatible with MathWorks Classification Learner app for further classification purposes such as model training, cross-validation scheme farming, and classification result computation.