Improving Brain E-Health Services via High-Performance EEG Classification With Grouping Bayesian Optimization

Online electroencephalograph (EEG) classification is a core service of recently booming brain e-health, but its performance often becomes unstable because (1) conventional end-to-end models (e.g., deep neural network, DNN) largely remain static, while brain states of diseases are highly dynamic and exhibits significant individuality; and (2) EEG analytics are too complicated and have to be sustained by advanced computing services. This study adopts an automatic machine learning method to construct a dual-CNN (convolutional neural network) of high performance in terms of both accuracy and efficiency. The model can optimize its hyperparameters continuously on its own initiative. Experimental results in the evaluation of depression using real EEG datasets indicate that (1) the proposed method executes 3.5 times faster compared with a conventional counterpart; (2) the dual-CNN gains a significant performance improvement (versus CapsuleNet and Resnet-16) in identifying Major Depression Disorder (MDD) with accuracy, sensitivity, and specificity up to 98.81, 98.36, and 99.31 percent respectively; and those for treatment outcome are 99.52, 99.63, and 99.37 percent respectively, and (3) classification can be completed several hundred times faster than EEG being collected upon a COTS computer.