2025/10/26 by Sarkar, Shantanu, Nabrzyski, Piotr, Prasad, Saurabh +1
Neuroscience · #Artificial Intelligence (cs.AI) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Neural dynamics and brain function #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2511.02848
openalex publication_date 2025/10/26 · openalex created_date 2025/11/07 · openalex updated_date 2026/07/28
Electroencephalography (EEG) is a widely used non-invasive technique for monitoring brain activity, but low signal-to-noise ratios (SNR) due to various artifacts often compromise its utility. Conventional artifact removal methods require manual intervention or risk suppressing critical neural features during filtering/reconstruction. Recent advances in generative models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have shown promise for EEG reconstruction; however, these approaches often lack integrated temporal-spectral-spatial sensitivity and are computationally intensive, limiting their suitability for real-time applications like brain-computer interfaces (BCIs). To overcome these challenges, we introduce EEGReXferNet, a lightweight Gen-AI framework for EEG subspace reconstruction via cross-subject transfer learning - developed using Keras TensorFlow (v2.15.1). EEGReXferNet employs a modular architecture that leverages volume conduction across neighboring channels, band-specific convolution encoding, and dynamic latent feature extraction through sliding windows. By integrating reference-based scaling, the framework ensures continuity across successive windows and generalizes effectively across subjects. This design improves spatial-temporal-spectral resolution (mean PSD correlation >= 0.95; mean spectrogram RV-Coefficient >= 0.85), reduces total weights by ~45% to mitigate overfitting, and maintains computational efficiency for robust, real-time EEG preprocessing in neurophysiological and BCI applications.