EEGReXferNet: A Lightweight Gen-AI Framework for EEG Subspace Reconstruction via Cross-Subject Transfer Learning and Channel-Aware Embedding

• URL: http://arxiv.org/abs/2511.02848v1
• Date: Sun, 26 Oct 2025 02:15:25 GMT
• Title: EEGReXferNet: A Lightweight Gen-AI Framework for EEG Subspace Reconstruction via Cross-Subject Transfer Learning and Channel-Aware Embedding
• Authors: Shantanu Sarkar, Piotr Nabrzyski, Saurabh Prasad, Jose Luis Contreras-Vidal,
• Abstract summary: We introduce EEGReXferNet, a lightweight framework for EEG subspace reconstruction via crosssubject-AI transfer learning.<n>EEGReXferNet employs volume conduction across neighboring channels, band-specific convolution encoding, and dynamic latent feature extraction through sliding windows.
• Score: 2.1349209400003937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• EUGens: Efficient, Unified, and General Dense Layers [56.498769704575544]
  We propose a new class of dense layers that generalize standard fully-connected feedforward layers, textbfEfficient, textbfUnimat and textbfGeneral dense layers (EUGens)<n>EUGens leverage random features to approximate standard FFLs and go beyond them by incorporating a direct dependence on the input norms in their computations.
  arXiv  Detail & Related papers  (2026-01-30T05:01:03Z)
• NeuroRVQ: Multi-Scale EEG Tokenization for Generative Large Brainwave Models [66.91449452840318]
  We introduce NeuroRVQ, a scalable Large Brainwave Model (LBM) centered on a codebook-based tokenizer.<n>Our tokenizer integrates: (i) multi-scale feature extraction modules that capture the full frequency neural spectrum; (ii) hierarchical residual vector quantization (RVQ) codebooks for high-resolution encoding; and, (iii) an EEG signal phase- and amplitude-aware loss function for efficient training.<n>Our empirical results demonstrate that NeuroRVQ achieves lower reconstruction error and outperforms existing LBMs on a variety of downstream tasks.
  arXiv  Detail & Related papers  (2025-10-15T01:26:52Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method [0.0]
  Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation.<n>This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm.
  arXiv  Detail & Related papers  (2025-07-28T17:09:48Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• ALFEE: Adaptive Large Foundation Model for EEG Representation [17.166788472910806]
  We propose the Adaptive Large Foundation model for EEG signal representation(ALFEE) framework.<n>ALFEE is a novel hybrid transformer architecture with two learning stages for robust EEG representation learning.<n>After 25,000 hours of pretraining, extensive experimental results on six downstream EEG tasks demonstrate the superior performance of ALFEE over existing models.
  arXiv  Detail & Related papers  (2025-05-07T13:32:31Z)
• An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling [2.1232375739287006]
  We propose EEGM2, a self-supervised framework for EEG representation learning.<n>EEGM2 achieves state-of-the-art performance in both short- and long-sequence modeling and classification.
  arXiv  Detail & Related papers  (2025-02-25T05:57:56Z)
• Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model [55.116403765330084]
  Current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. We propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros.
  arXiv  Detail & Related papers  (2024-04-08T16:34:35Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [73.50359502037232]
  VoxNeRF is a novel approach to enhance the quality and efficiency of neural indoor reconstruction and novel view synthesis.<n>We propose an efficient voxel-guided sampling technique that allocates computational resources to selectively the most relevant segments of rays.<n>Our approach is validated with extensive experiments on ScanNet and ScanNet++.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• Evolving Connectivity for Recurrent Spiking Neural Networks [8.80300633999542]
  Recurrent neural networks (RSNNs) hold great potential for advancing artificial general intelligence. We propose the evolving connectivity (EC) framework, an inference-only method for training RSNNs.
  arXiv  Detail & Related papers  (2023-05-28T07:08:25Z)
• An intertwined neural network model for EEG classification in brain-computer interfaces [0.6696153817334769]
  The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. We present a deep neural network architecture specifically engineered to provide state-of-the-art performance in multiclass motor imagery classification.
  arXiv  Detail & Related papers  (2022-08-04T09:00:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.