Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning

Abstract

Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction techniques are commonly in use to extract statistically independent features from experimental data for regression against model parameters. Here, we first designed a 3-dimensional feature space based on commonly described event-related potentials (ERP) from the literature. We then compared classification schemes which take as inputs either the 3D projection space or the original full rank input space. Their ability to discriminate TEP recorded from different brain regions given a stimulus site were evaluated. We show that a deep learning architecture, employing Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), yields better accuracy than the 3D projection and raw TEP input combined with Support Vector Machines. Such supervised feature extraction models may therefore be useful for scoring neural circuit simulations based on their ability to reproduce the underlying dynamical processes responsible for differential TEP responses.