arXiv Machine Learning

Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment

Prism‑SQA is a neural framework that interprets surface electromyography (sEMG) quality assessment by separating each signal into a clean component and five contaminant-specific components using a U‑Net with bidirectional LSTM. Each contaminant is verified against canonical physiological signatures, allowing clinicians to see how specific noises affect quality. The resulting quality indices can be customized for different clinical contexts without retraining, and Prism‑SQA matches or surpasses current black‑box methods on both continuous and binary quality tasks.

arXiv AI

Sep 15

EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

EEG-Xplain introduces a unified attribution framework to interpret EEG foundation models such as BIOT, LaBraM, and EEGMamba. The framework combines gradient, perturbation, and activation-based methods to analyze model behavior across spatial, temporal, and frequency dimensions, identifying critical channels, decision-relevant signal segments, and contributions of canonical EEG rhythms. It evaluates explanation reliability with population-level metrics and uses large language models to convert structured attributions into natural-language reports, demonstrating consistency with known neurophysiological markers on benchmark datasets.

By Hansong Ma, Junxiao Wang

llms benchmarks safety

arXiv AI

Jul 28

Neonatal Hypoxic-ischaemic Encephalopathy Classification from the EEG and HRV Signals Using a Conformer based Masked Autoencoder

arXiv:2607. 23554v1 Announce Type: cross Abstract: In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals.

By Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody

llms benchmarks

arXiv Machine Learning

Aug 4

nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI

arXiv:2605. 14941v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio, which makes extraction of meaningful neural information challenging.

By Shantanu Sarkar, Jose L. Contreras-Vidal

multimodal benchmarks

arXiv AI

Jun 2

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.

By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed

benchmarks

arXiv AI

Sep 1

A foundation model with multi-variate parallel attention to generate neuronal activity

arXiv:2506.20354v3 Announce Type: replace-cross Abstract: Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, p...

By Francesco Carzaniga, Michael Hersche, Abu Sebastian, Kaspar Schindler, Abbas Rahimi

llms benchmarks

arXiv AI

Jun 16

CAP: Towards PPG Universal Representation Learning with Patient-level Supervision

arXiv:2606. 15284v1 Announce Type: cross Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support.

By Chenyang He, Xinyi Shao, Shun Huang, Bosong Huang, Daoqiang Zhang, Ming Jing, Cheng Ding

multimodal benchmarks safety

arXiv AI

Aug 26

Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks

arXiv:2608.24597v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised...

By Yulong Dou, Han Wu, Guo Chen, Fangmao Ju, Zhiming Cui, Dinggang Shen

fine-tuning benchmarks

arXiv Machine Learning

Aug 17

EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.

By Deeksha M Shama, Punnisa Amornsirikul, Archana Venkataraman

safety

arXiv AI

Jul 31

How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

arXiv:2601. 10191v2 Announce Type: replace Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis.

By Mathieu Cherpitel, Janne Luijten, Thomas B\"ack, Camiel Verhamme, Martijn Tannemaat, Anna V. Kononova

arXiv AI

Jun 8

LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

arXiv:2603. 19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity.

By Dana\'e Broustail, Anna Tegon, Thorir Mar Ingolfsson, Yawei Li, Luca Benini

llms rag

arXiv AI

Jun 2

CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention

arXiv:2606. 00074v1 Announce Type: cross Abstract: Reliable seizure prediction is a prerequisite for closed-loop neurostimulation therapy, yet existing methods rarely account for the variability in EEG signal quality encountered in real-world deployment, and the overwhelming majority adopt non-strict evaluation protocols that overestimate generalisation performance.

By Mufeng Chen, Qi Wu, Bingchao Huang, Xiwen Lai, Zekai Chen, Xinge Ouyang, Quansheng Ren

fine-tuning

arXiv AI

Jun 29

Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes

arXiv:2606. 27855v1 Announce Type: cross Abstract: Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders.

By Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis

fine-tuning benchmarks

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