EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss - Luo, Tian-jian; Fan, Yachao; Chen, Lifei; Guo, Gongde; Zhou, Changle (Frontiers in Neuroinformatics, 2020)
Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance versus low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rate and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function suppresses noise and reconstructed signals by calculating the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising results were obtained from three action-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method improves the classification performance of low sampling rate and low sensitivity signals and denoised the noisy signals. By introducing WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost, and is convenient for the design of high-performance brain computer interface systems.