Support Vector Regression Prediction of Graded FMRI Activity

Yash Shailesh Shah¹, Douglas C. Noll, Scott J. Peltier

Support Vector Regression is a machine learning technique that learns the mapping from the training set and labels provided. This creates a model which can then be used to give predictions for all testing sets. The prediction is really quick and hence SVR has potential to be used as a tool for real-time biofeedback applications to evaluate graded potential. In this study, we have used SVR analysis to evaluate graded activation in multiple neural systems namely the visual and motor cortex activation. The outputs are encouraging and advocate prospects of using SVR for future work in building real-time biofeedback applications in which graded activation needs to be evaluated.