Application of neural network and SVM to classify movement of rat in medical science

ABSTRACT: Identifying mammalian movement states has recently become an important topic in biological science research. Accurate assessment and analysis of movement is a fundamental requirement. Rodents are often used as models in the sleep field due to their ready availability and the similarities of their movement to human movement. The goal of intra-cortical brain computer interface (BCI) is to restore the lost functionalities in disabled patients suffering from severely impaired movements. 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, Hit or No Hit.