R-CNN-TPOT: a new hybrid machine learning network for brain age prediction using EEG signal.

Abstract

Brain age refers to the significant changes in electroencephalogram (EEG) signals that occur as people age. The chronological age can be compared to the brain age to determine the variations from the normal ageing process. With the rise of Machine Learning (ML), many brain age prediction methods have been developed using brain imaging. However, EEG-based approaches remain underexplored and have not utilized the Tree-based Pipeline Optimization Tool (TPOT). To subdue this problem, a novel hybrid ML technique is proposed for predicting brain age from EEG signals. The proposed method uses different features, such as spectral features, statistical features, frequency domain features and decomposition domain features. Additionally, a new ML approach called Regression-based Convolutional Neural Network-TPOT (R-CNN-TPOT) has been developed to perform the task of brain age prediction. Here, R-CNN-TPOT is obtained by combining the mathematical model of the Convolutional Neural Network (CNN) model and TPOT classification using regression modelling. In addition, the devised R-CNN-TPOT model provides better output with a Mean Absolute Error (MAE) of 0.033, Mean Square Error (MSE) of 0.063, R-squared of 15.456, and Root MSE (RMSE) of 0.251.