Electroencephalography-based Motor Hotspot Detection

Abstract

The motor-evoked potential (MEP) induced by transcranial magnetic stimulation (TMS) has been generally used to identify a motor hotspot, and it has been used as a target location for transcranial electrical stimulation (tES). However, the traditional MEP-based method needs a bulky TMS device, and it involves the empirical judgement of an expert. In this study, we propose a machine-learning-based motor hotspot identification method using electroencephalography (EEG) that is portably acquired in a tES device. EEG data were measured from ten subjects while they performed a simple finger tapping task. Power spectral densities (PSDs) were extracted from the EEG data as features, and they were used to train and test artificial neural network (ANN). The 3D coordinate information of individual motor hotspots identified by TMS were also used as the ground-truth motor hotspot locations in ANN, and they were compared with those estimated by ANN. A minimum distance between the motor hotspots identified by TMS and EEG features was only 0.24 cm, demonstrating the feasibility of our proposed novel motor hotspot identification method based on EEG features.