 This paper proposes a novel hybrid convolutional and temporal, convolutional neural network, CNNTCN, to continuously estimate depression scores based on raw EEG signals. The CNNTCN outperforms existing deep learning methods and provides more accurate estimates, compared to conventional EEG feature extraction methods. This article was authored by S. Hashempur, Arbustani, M. Mohamedi, and others.