The goal of intra-cortical brain computer interface (BCI) is to restore the lost functionalities in disabled patients suffering from severely impaired movements. BCIs have the advantage to create a direct communication pathway between the brain and an external device for restoring disability. This is possible by decoding signals from the primary motor cortex and translating them into commands for a prosthetic device. The project aim was to develop a decoding method based on a rat model. Previously recorded data and an already develop pre-processing method were used. The experimental design was developed starting from intra-cortical (IC) signal recorded in the rat primary motor cortex (M1). The data pre-processing included denoising with wavelet technique, spike detection, and feature extraction. After the firing rates of intra-cortical neurons were extracted, artificial neural network (ANN) and support vector machine (SVM) were applied to classify the rat movements into two possible classes, \textit{Hit} or \textit{No Hit}. The misclassification error rates obtained from denoised and not denoised data were statistically different (p<0.05), proving the efficiency of the denoising technique. ANN and SVM provided comparable classification errors, ranging between 14\% and 39\%.