IGANEEG: an IoMT-enabled Generative Framework for Small-Sample Schizophrenia EEG Augmentation

In the Internet of Medical Things (IoMT), reliable electroencephalogram (EEG) analysis is crucial for early diagnosis of schizophrenia and other neurological disorders. However, limited schizophrenia EEG data restrict the generalization ability of deep learning models, hindering clinical deployment. Existing augmentation methods often produce low-quality synthetic samples or suffer from overfitting and vanishing gradients. To address these issues, we propose IGANEEG, an improved generative adversarial network for small-sample schizophrenia EEG augmentation. IGANEEG incorporates joint time-frequency-spatial feature groups from real EEG as conditional inputs to generate realistic synthetic samples retaining critical data characteristics. The generator is replaced by an autoencoder to reduce reconstruction loss, and a dynamic pause mechanism is proposed to enhance training stability while the learning capacity of the hidden layers is optimized. Experiments on a public dataset and a private dataset show that IGANEEG achieves recognition accuracies of 96.8% and 98.2%, respectively, substantially outperforming state-of-the-art methods. These results validate the framework's effectiveness in improving model generalization and offer valuable data support for intelligent healthcare systems in the IoMT.