NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models

Konstantinos Barmpas^1,2   Na Lee^1,2   Dimitrios Chalatsis¹   William Raftery¹  

Yannis Panagakis^2,3,4   Dimitrios Adamos^1,2   Nikolaos Laskaris^2,5  

Alexandros Koliousis⁶   Dario Farina¹   Stefanos Zafeiriou^1,2

¹Imperial College London, United Kingdom
²Cogitat, United Kingdom
³National and Kapodistrian University of Athens, Greece
⁴Archimedes Research Unit, Greece
⁵Aristotle University of Thessaloniki, Greece
⁶Northeastern University London, United Kingdom

This is the official implementation of NeuroRVQ, a foundation model for biosignals powered by a state-of-the-art biosignal tokenizer.

Visit the official website for more information: https://neurorvq.github.io

🌟 Overview

Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, yielding representations that are rich but challenging for machine learning. Foundation models trained to predict masked signal tokens have shown promise in learning generalizable biosignal representations, yet their performance depends on the tokenizer's ability to preserve high-frequency dynamics and reconstruct signals with high fidelity. We introduce NeuroRVQ, a modality-adaptive biosignal tokenizer family designed for high-fidelity signal reconstruction. To capture the full frequency spectrum, NeuroRVQ decomposes biosignals into frequency-specific representations via multi-scale temporal convolutions, each encoded into hierarchical RVQ codebooks to preserve high-frequency detail, combined with a novel phase-aware training loss that respects the circular topology of Fourier phase. By tuning the temporal resolution, number and size of temporal kernels and RVQ depth, this design adapts to the spectro-temporal characteristics of each biosignal modality. To validate that tokenizer quality drives downstream performance, we train a simple masked-token foundation model for each modality (NeuroRVQ-FM) using the corresponding NeuroRVQ tokenizer. The NeuroRVQ-FM family achieves competitive or superior downstream performance compared to existing modality-specific foundation models, demonstrating that high-fidelity tokenization is a critical factor for effective biosignal modeling.

NeuroRVQ Tokenizer converts raw biosignals into compact and informative neural tokens. The input multi-variate time series is segmented into patches, encoded by the multi-scale temporal encoder at multiple resolutions, combined via a transformer encoder, then discretized into neural tokens through per-scale RVQ codebooks. Tokens are decoded to reconstruct the input patches using the Fourier spectrum.

NeuroRVQ Foundation Model operates on the tokenized representation, using masked-token prediction with symmetric masking. By working at the token level, it captures long-range dependencies, learns abstract neural dynamics, and enables efficient pre-training across diverse biosignal datasets. The learned codebooks serve as prediction targets during pre-training, and the resulting representations transfer effectively to a range of downstream BCI tasks.

Model and Modalities

Modality	Support
EEG	✅
EMG	✅
ECG	✅

Model Version	Parameters	Modality	Trained Models
NeuroRVQ-EEG-tokenizer-v1	76 Million	EEG	NeuroRVQ_EEG_tokenizer_v1.pt
NeuroRVQ-EEG-foundation-model-v1	6 Million	EEG	NeuroRVQ_EEG_foundation_model_v1.pt
NeuroRVQ-EMG-tokenizer-v1	144 Million	EMG	NeuroRVQ_EMG_tokenizer_v1.pt
NeuroRVQ-EMG-foundation-model-v1	6 Million	EMG	NeuroRVQ_EMG_foundation_model_v1.pt
NeuroRVQ-ECG-tokenizer-v1	76 Million	ECG	NeuroRVQ_ECG_tokenizer_v1.pt
NeuroRVQ-ECG-foundation-model-v1	6 Million	ECG	NeuroRVQ-ECG-foundation-model-v1

Tokenization / Reconstruction Capabilities

EEG	ECG	EMG

Downstream Performance

EEG

Model	Motor	ERP	Memory	Sleep*	Eyes	Mean	Size
NeuroGPT	0.682±0.083	0.757±0.048	0.597±0.029	0.674±0.033	0.827±0.036	0.707±0.046	79.5M
CBraMod	0.614±0.104	0.777±0.052	0.574±0.038	0.635±0.041	0.839±0.041	0.688±0.055	4.9M
BIOT	0.443±0.079	0.500±0.000	0.510±0.018	--	0.763±0.049	--	3.2M
MIRepNet	0.689±0.086	--	--	--	--	--	--
LaBraM	0.630±0.076	0.822±0.040	0.526±0.026	0.652±0.037	0.799±0.047	0.686±0.045	5.8M
EEGPT	0.313±0.035	0.668±0.146	0.520±0.017	0.634±0.044	0.797±0.037	0.587±0.056	25.7M
NeuroRVQ	0.700±0.073	0.876±0.033	0.574±0.027	0.728±0.028	0.869±0.026	0.749±0.037	5.9M

We used the benchmark presented in IEEE MLSP 2025 Paper Assessing the Capabilities of Large Brainwave Foundation Models.

Open-Source Benchmark Code Available

ECG

Model	5-class Accuracy	5-class BAcc	43-class Accuracy	43-class BAcc
HuBERT-ECG	72.60	60.23	62.49	20.71
ECGFounder	76.55	65.39	65.51	28.96
NeuroRVQ-ECG	70.19	64.50	79.17	58.33

Open-Source Benchmark Code Available

EMG

Model	DG BAcc ↑	DG CLER ↓	EPN-612 Acc	EPN-612 F1	NinaPro DB5 Acc	NinaPro DB5 F1	UCI-EMG Acc	UCI-EMG F1
PhysioWave	54.70	64.20	90.30	90.35	24.91	22.95	56.52	55.76
TinyMyo	39.70	64.20	84.68	84.68	25.26	23.29	85.99	85.66
NeuroRVQ-EMG	70.80	27.60	94.65	94.66	41.36	38.76	89.43	89.28

Installation

conda create -n neurorvq python=3.10
conda activate neurorvq

# Install requirements
pip install -r requirements.txt

Download Models

The models and the sample biosignal for reconstruction demos can be downloaded manually from HuggingFace or using python:

from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="pretrained_models/tokenizers/NeuroRVQ_EEG_tokenizer_v1.pt", local_dir="./")
hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="pretrained_models/foundation_models/NeuroRVQ_EEG_foundation_model_v1.pt", local_dir="./")
hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="pretrained_models/tokenizers/NeuroRVQ_EMG_tokenizer_v1.pt", local_dir="./")
hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="pretrained_models/foundation_models/NeuroRVQ_EMG_foundation_model_v1.pt", local_dir="./")
hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="example_files/eeg_sample/example_eeg_file.xdf", local_dir="./")
hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="pretrained_models/tokenizers/NeuroRVQ_ECG_tokenizer_v1.pt", local_dir="./")
hf_hub_download(repo_id="ntinosbarmpas/NeuroRVQ", filename="pretrained_models/foundation_models/NeuroRVQ_ECG_foundation_model_v1.pt", local_dir="./")

Model Loading / Usage

Load EEG tokenizer and see reconstruction results. Example for EEG tokenizer:

from inference.run.NeuroRVQ_EEG_tokenizer_example import load_neurorqv_tokenizer

# Set run_example=True and plot_results=True to see reconstruction results
# Checkout the load_neurorqv_tokenizer() function to load and use tokenizer

load_neurorqv_tokenizer(run_example=True, plot_results=True, verbose=True,
                            model_path='./pretrained_models/tokenizers/NeuroRVQ_EEG_tokenizer_v1.pt')

Load foundation model and see an example for fine-tuning. Example for EEG foundation model:

from inference.run.NeuroRVQ_EEG_FM_example import load_neurorqv_fm

# Checkout the load_neurorqv_fm() function with fine_tuning=False to see the correct model loading
# See the instructions in data.py for your custom dataset before setting fine_tuning=True

load_neurorqv_fm(fine_tuning=False, verbose=True,
                     model_path = './pretrained_models/foundation_models/NeuroRVQ_EEG_foundation_model_v1.pt')

Load EMG tokenizer and see reconstruction results (downloads mini version of emg2pose). Example for EMG tokenizer:

from inference.run.NeuroRVQ_EMG_tokenizer_example import load_neurorqv_tokenizer

# Set run_example=True and plot_results=True to see reconstruction results
# Checkout the load_neurorqv_tokenizer() function to load and use tokenizer

load_neurorqv_tokenizer(run_example=True, plot_results=True, verbose=True,
                            model_path='./pretrained_models/tokenizers/NeuroRVQ_EMG_tokenizer_v1.pt')

Load foundation model and see an example for fine-tuning. Example for EMG foundation model:

from inference.run.NeuroRVQ_EMG_FM_example import load_neurorqv_fm

# Checkout the load_neurorqv_fm() function with fine_tuning=False to see the correct model loading
# See the instructions in data.py for your custom dataset before setting fine_tuning=True

load_neurorqv_fm(fine_tuning=False, verbose=True,
                     model_path = './pretrained_models/foundation_models/NeuroRVQ_EMG_foundation_model_v1.pt')

Load ECG tokenizer and see reconstruction results (downloads and processes ptb-xl dataset). Example for ECG tokenizer:

from inference.run.NeuroRVQ_ECG_tokenizer_example import load_neurorqv_tokenizer

# Set run_example=True and plot_results=True to see reconstruction results
# Checkout the load_neurorqv_tokenizer() function to load and use tokenizer

load_neurorqv_tokenizer(run_example=True, plot_results=True, verbose=True,
                            model_path='./pretrained_models/tokenizers/NeuroRVQ_ECG_tokenizer_v1.pt')

Load foundation model and see an example for fine-tuning. Example for ECG foundation model:

from inference.run.NeuroRVQ_ECG_FM_example import load_neurorqv_fm

# Checkout the load_neurorqv_fm() function with fine_tuning=False to see the correct model loading
# See the instructions in data.py for your custom dataset before setting fine_tuning=True

load_neurorqv_fm(fine_tuning=False, verbose=True,
                     model_path = './pretrained_models/foundation_models/NeuroRVQ_ECG_foundation_model_v1.pt')

Citation

@misc{neurorvq,
      title={NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models},
      author={Konstantinos Barmpas and Na Lee and Dimitrios Chalatsis and William Raftery and Yannis Panagakis and Dimitrios A. Adamos and Nikolaos Laskaris and Alexandros Koliousis and Dario Farina and Stefanos Zafeiriou},
      year={2026},
      eprint={2510.13068},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.13068},
}