Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire.Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals.Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score.In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals.Moreover, all the subjects take the BDI test and their scores are determined.The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27for eyes-open state and also provides MSE of 9.53±2.94and MAE of 2.32±0.35for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods.In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods.Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59that statistically outperform the statistical regression methods.Moreover, the results with raw EEG are significantly better than those with EEG features.