The Analysis of the Accuracy of Support Vector Regression Classification of a Neural Dataset Originating in the Motor Cortex of a Macaque Monkey

Abstract

Neural decoding is a constantly adapting field of applied machine learning, using many different machine learning algorithms to analyze neural data. One such analysis of neural data is the interpretation and analysis of data originating the motoro cortex and the prediction of stimuli given to an organism using the motor response. In looking at a specific dataset, containing data from the motor cortex of a macaque monkey, where its neural response to directional stimuli was measured. Two different neural decoding algorithms were previously used to analyze this dataset, yet the highest accuracy they yielded was below 90%. There is a need for machine-learning based neural decoding algorithms to decode movement-relating neural data with higher accuracy. Support vector regression (SVR), a linear-regression based model of the machine learning algorithm support vector machines, was chosen for analysis. In this study, we aim to evaluate the predictive accuracy of SVR using a dataset obtained from a macaque monkey. The model predicted the directional stimuli with an accuracy of 82.47% percent.