Electronic Artificial Intelligence–Digital Twin Model for Optimizing Electroencephalogram Signal Detection

Abstract

The study is focused on the application of the electronic proof of concept Digital Twin (DT) model supporting Electroencephalogram (EEG) signal detection and interpretation. The EEG DT model integrates two open source tools: a first tool used for the circuit modeling and simulation of the electrodes, and a second one implementing an Artificial Intelligence (AI)-supervised algorithm to classify and adjust a noisy EEG signal. Specifically, the DT model adopts the Random Forest (RF) AI-supervised algorithm, replacing the signal filtering process and facilitating the time–domain peak and the wave shape morphology reading of a noisy detection. In order to prove the DT’s efficacy, the RF model is trained by considering the specific case of detections of EEG of patients under the effects of alcohol. The choice of the RF algorithm is justified by its good performance parameters. For the specific dataset, the RF exhibits a probabilistic error slightly lower than that of the ANN and a better cleaning action. The goal of the paper is to provide a methodology to use ‘intelligent’ electrodes supporting EEG data processing during data acquisition and to optimize the measurement’s interpretation through a data post-processing process. The proposed EEG DT could represent an alternative to the traditional denoising signal processing approaches.