A spatiotemporal fused network considering electrode spatial topology and time-window transition for MDD detection

Recently, researchers have begun to experiment with deep learning-based methods for detecting major depressive disorder (MDD) using electroencephalogram (EEG) signals in search of a more objective means of diagnosis. However, existing spatiotemporal feature extraction methods only consider the functional correlation between multiple electrodes and temporal correlation of EEG signals, ignoring the spatial position connection information between electrodes and the continuity between time windows, which reduces the model's feature extraction capabilities. To address this issue, a Spatiotemporal fused network for MDD detection with Electrode spatial Topology and adjacent TIME-window transition information (SETTIME) is proposed in this study. SET-TIME is composed by a common feature extractor, a secondary time-correlation feature extractor, and a domain adaptation (DA) module, in which the former extractor is used to obtain the temporal and spatial features, and the latter extractor can mine the correlation between multiple time windows, and the DA module is adopted to enhance cross-subject detection capability. The experimental results of 10-fold cross-validation show that the proposed SETTIME method outperforms the state-of-the-art (SOTA) method by achieving MDD detection accuracies of 92.00% and 94.00% on the public datasets PRED+CT and MODMA, respectively. Ablation experiments demonstrate the effectiveness of the multiple modules in SET-TIME, which assist in MDD detection by exploring the intrinsic spatiotemporal information of EEG signals.