NeurIPT: Foundation Model for Neural Interfaces

• URL: http://arxiv.org/abs/2510.16548v1
• Date: Sat, 18 Oct 2025 15:45:00 GMT
• Title: NeurIPT: Foundation Model for Neural Interfaces
• Authors: Zitao Fang, Chenxuan Li, Hongting Zhou, Shuyang Yu, Guodong Du, Ashwaq Qasem, Yang Lu, Jing Li, Junsong Zhang, Sim Kuan Goh,
• Abstract summary: NeurIPT is a foundation model developed for diverse EEG-based Neural Interfaces with a Pre-trained Transformer.<n>We introduce Amplitude-Aware Masked Pretraining (AAMP) to learn robust representations across varying signal intensities beyond local.<n>We also develop Intra-Inter Lobe Pooling (IILP) during fine-tuning to efficiently exploit regional brain features.
• Score: 13.659630322026095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalography (EEG) has wide-ranging applications, from clinical diagnosis to brain-computer interfaces (BCIs). With the increasing volume and variety of EEG data, there has been growing interest in establishing foundation models (FMs) to scale up and generalize neural decoding. Despite showing early potential, applying FMs to EEG remains challenging due to substantial inter-subject, inter-task, and inter-condition variability, as well as diverse electrode configurations across recording setups. To tackle these open challenges, we propose NeurIPT, a foundation model developed for diverse EEG-based Neural Interfaces with a Pre-trained Transformer by capturing both homogeneous and heterogeneous spatio-temporal characteristics inherent in EEG signals. Temporally, we introduce Amplitude-Aware Masked Pretraining (AAMP), masking based on signal amplitude rather than random intervals, to learn robust representations across varying signal intensities beyond local interpolation. Moreover, this temporal representation is enhanced by a Progressive Mixture-of-Experts (PMoE) architecture, where specialized expert subnetworks are progressively introduced at deeper layers, adapting effectively to the diverse temporal characteristics of EEG signals. Spatially, NeurIPT leverages the 3D physical coordinates of electrodes, enabling effective transfer of embedding across varying EEG settings, and develops Intra-Inter Lobe Pooling (IILP) during fine-tuning to efficiently exploit regional brain features. Empirical evaluations across eight downstream BCI datasets, via fine-tuning, demonstrated NeurIPT consistently achieved state-of-the-art performance, highlighting its broad applicability and robust generalization. Our work pushes forward the state of FMs in EEG and offers insights into scalable and generalizable neural information processing systems.

Related papers

• 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)
• EEGDM: EEG Representation Learning via Generative Diffusion Model [17.595769291603688]
  We propose an EEG representation learning framework building upon Generative Diffusion Model (EEGDM)<n>Specifically, we developed a structured state-space model for diffusion pretraining and trained it using Denoising Diffusion Probabilistic Model (DDPM) framework.<n>The resulting latent EEG representations were then used for downstream classification tasks via our proposed latent fusion transformer (LFT)
  arXiv  Detail & Related papers  (2025-08-13T14:40:52Z)
• A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis [2.355460994057843]
  This study proposes a novel and unified deep learning framework that achieves state-of-the-art performance across different signal types.<n>Unlike prior work, we scientifically increase signal complexity to achieve future-reaching capabilities, which resulted in the best predictions.<n>The architecture requires 130 MB of memory and processes each sample in 10 ms, suggesting suitability for deployment on low-end or wearable devices.
  arXiv  Detail & Related papers  (2025-07-16T21:38:10Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals [46.121056431476156]
  This paper proposes Brain Omni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings.<n>Existing approaches typically rely on separate, modality- and dataset-specific models, which limits performance and cross-domain scalability.<n>A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining.
  arXiv  Detail & Related papers  (2025-05-18T14:07:14Z)
• 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)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling [19.85701025524892]
  FoME (Foundation Model for EEG) is a novel approach using adaptive temporal-lateral attention scaling. FoME is pre-trained on a diverse 1.7TB dataset of scalp and intracranial EEG recordings, comprising 745M parameters trained for 1,096k steps.
  arXiv  Detail & Related papers  (2024-09-19T04:22:40Z)
• RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier [0.0]
  Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications.<n>We introduce a novel decoder model that is robust to inter-subject electrode implantation variability.<n>We call this model RISE-iEEG, which stands for Robust to Inter-Subject Electrode Implantation Variability iEEG.
  arXiv  Detail & Related papers  (2024-08-12T18:33:19Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)

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.