High-Fidelity EEG Generation: Generative Adversarial Network Highlighting Time-Frequency-Spatial Features Regulated...

Electroencephalogram (EEG) analysis has heavily relied on sophisticated machine learning methods. However, the limited availability of diverse and extensive EEG datasets often underscores the need for reliable data augmentation approaches. This study introduces a new Generative Adversarial Network framework, HiFi-EEG-GAN, consisting of a supervisor, generator, and discriminator, aiming at generating artificial EEG that closely mimics real-world counterparts with "high fidelity" (Hi-Fi). The framework emphasizes two core tasks: 1) Global Dynamics Supervision: The supervisor model distills the global dynamics of real EEG into a Gaussian-like representation. This representation regulates the subsequent EEG generation using Kullback-Leibler (KL) divergence, focusing on macroscopic dynamics; and 2) Hi-Fi EEG Generation: EEG generator replicates real EEG's time, frequency, and spatial characteristics using a composite architecture. This process is further regulated by another discriminator, focusing on microscopic details. The HiFi-EEG-GAN framework (design validated through ablation study) outperforms state-of-the-art counterparts (e.g., FT-Surrogate, EEG-GAN, BWGAN-GP) in terms of fidelity and diversity in data augmentation. Notable performance metrics include r1NNC (0.88), FID (13.97), and MMD (0.09). In two test cases, classification accuracy improves by 3.2% 6.8% in ASD and 2.5% 8.2% in mental arithmetic tasks, surpassing its counterparts with EEG augmentation by HiFi-EEG-GAN.