Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset - Khayretdinova, Mariam; Shovkun, Alexey; Degtyarev, Vladislav; Kiryasov, Andrey; Pshonkovskaya, Polina; Zakharov, Ilya (Frontiers in Aging Neuroscience, 2022)
Brain age prediction has been shown to be clinically relevant, with the errors in the prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear. The current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1265 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and a developed deep convolutional neural network model. The model’s training took place with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in the cross-validation. We found that the best performance can be achieved when both eye open and eyes closed states are used simultaneously. The fronto-central electrodes played the most important role in age prediction. The proposed deep convolutional neural networks (DCNN) architecture and training method improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.