Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset

Abstract

IntroductionBrain age prediction has been shown to be clinically relevant, with errors in its 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.MethodsThe 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 1,274 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 developed a deep convolutional neural network model.ResultsThe model’s training was done 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 cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction.DiscussionThe architecture and training method of the proposed deep convolutional neural networks (DCNN) 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.

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

“…We selected it primarily for comparison because of its utilization of the CNN algorithm, a feature aligned with our current study. However, it is important to note that the pyment model was developed using a significantly larger, multisite dataset (n = 53,542), and thus surpassed various brain aging models, including ours, with an MAE of 2.47 23,30,86 . For this comparison, we employed a new, independent dataset comprising 200 healthy individuals (mean age = 57.6 years, SD = 23.0 years, range = 18.0-90.0 years; consisting of 93 men and 107 women) sourced from the ADNI1 and OASIS-1 databases, as the CamCAN dataset had been previously used in training the pyment model.…”

SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering

“…For instance, Kaur and colleagues applied the random forest algorithm to predict age and gender [11], achieving an age classification accuracy of 88.33% and a gender classification accuracy of 96.66%. Around the 2020s, deep learning technology made breakthrough progress in this area [12][13][14]. Among these, Jusseaume et al utilized Long Short-Term Memory (LSTM) networks to analyze EEG records of epilepsy patients [13], successfully predicting the patients' brain age with an accuracy of up to 90%.…”

Early Detection of Stress and Anxiety Based Seizures in Position Data Augmented EEG Signal Using Hybrid Deep Learning Algorithms

“…DNN achieves superior accuracy. In [15], the authors focussed on data augmentation techniques for age prediction, enhances the model performance through increased training dataset. Band-pass filter is used for preprocessing and utilized batch normalization techniques.…”

“…Abnormalities in signal are effectively preserved through frequency components within the desired range and reduces the amplitudes of frequencies outside the band. Bandpass filter[15] attenuates frequencies outside the desired range and reduces the amplitudes of frequencies outside the band, leads to minimal distortion to EEG signals within the passband. This characteristic leads to integrity and accuracy of the EEG signal during analysis.…”