Major Depressive Disorder (MDD) affects a wide range of populations and causes significant harm to individuals and society. Hence, early recognition of MDD is crucial. MDD recognition using wearable electroencephalographic (EEG) devices has gained significant attention, with reliable and effective classification algorithms central to its success.

Herein, an end-to-end framework named an end-to-end shuttle neural network (ESNN), is proposed for efficient recognition of depression on multichannel EEG signals. The ESNN comprises three parts: i) a multiscale saliency–encoded spectrogram that effectively captures time–frequency information from multichannel EEG signals; ii) TSUnet, a two-stream temporal spectrogram U-Net incorporating the crossmodule attention to redistribute feature weights and enhance critical information; and iii) a crosschannel-wise block to integrate time–frequency features from the two-stream network.

Two public EEG datasets [the Hospital Universiti Sains Malaysia (HUSM) and MODMA)] and one private EEG dataset [Zhongda Hospital, Southeast University (ZHSU)] were used to confirm the model's performance. The leave-one-subject-out validation experiment was conducted to ensure subject independence. Our proposed ESNN achieved accuracies of 98.70% and 86.36% on HUSM and MODMA datasets, respectively. On ZHSU dataset, the framework remarkably performed with 83.85% accuracy.

The results verified that different scale features could be adequately captured by branch processing and fusion of time–frequency information. Ablation experiments also suggested that the proposed crossmodule attention and channel-wise block effectively focused significant information, suggesting that this model could potentially recognize depression in a real-world scenario. Our model exhibits the potential for application as a clinical decision support tool. By assisting physicians in diagnosis, it contributes to the conservation of healthcare resources. The code is provided in: ESNN .