arXiv AI

The Bioelectrical Information Theory: Investigating the theoretical compression limit of bioelectrical signals under artificial intelligence

arXiv:2606. 09922v1 Announce Type: cross Abstract: Bioelectrical signals are increasingly acquired at scales that challenge the bandwidth of brain-computer interfaces.

arXiv AI

Jun 9

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.

By Jonathan F. Carter, Lionel Tarassenko

llms rag efficiency multimodal benchmarks

arXiv AI

Jun 19

SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models

arXiv:2606. 19888v1 Announce Type: cross Abstract: Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data.

By Feng Wu, Harsh Deep, Eric Lehman, Sanyam Kapoor, Guoshuai Zhao, Rahul Krishnan, Gari Clifford, Li-wei H Lehman

benchmarks

arXiv Machine Learning

Jun 2

Robust Frequency-Calibrated Virtual EEG Channel Generation from Four Frontal Electrodes for Wearable EEG Augmentation

arXiv:2605. 29263v2 Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased view of distributed scalp activity.

By Minghao Xiao

rag

arXiv Machine Learning

Jul 17

Gibbs randomness-compression proposition

arXiv:2505. 23869v4 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a compression process.

By M. S\"uzen

efficiency

arXiv AI

Jul 16

Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals

arXiv:2510. 15371v2 Announce Type: replace-cross Abstract: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments.

By Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura

llms benchmarks

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

Jul 14

Event-based Neural Decoding for Neuroprosthetic Motor Control

arXiv:2607. 11445v1 Announce Type: new Abstract: A substantial number of patients experience diminished mobility due to disabilities, diseases, or accidents.

By Khaleelulla Khan Nazeer, Sirine Arfa, Matthias Jobst, Richard George, Christian Mayr

benchmarks

arXiv Machine Learning

Jul 15

Same Compression Principle, Different Geometry: Rate-Distortion Signatures Dissociate Biological and Artificial Visual Systems

arXiv:2603. 01568v2 Announce Type: replace Abstract: Efficient coding theory predicts that biological perceptual systems compress sensory input optimally under resource constraints, with the systematic structure of errors reflecting the geometry of that compression.

By Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin

arXiv Machine Learning

Jul 7

Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding

arXiv:2508. 11657v2 Announce Type: replace-cross Abstract: Objective: Sparse Bayesian learning provides an effective framework to solve high-dimensional problems in brain signal decoding.

By Yuanhao Li, Badong Chen, Wenjun Bai, Yasuharu Koike, Okito Yamashita

arXiv Machine Learning

Jul 16

Leveraging unlabelled data for generalizable neural population decoding

arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.

By Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo, Matthew G. Perich, Guillaume Lajoie

fine-tuning benchmarks

arXiv Machine Learning

Jul 7

Prior-Guided Frequency-Calibrated Virtual EEG Channel Inference from Four Frontal Electrodes for Wearable EEG Augmentation

arXiv:2605. 29263v3 Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased sampling of the scalp potential field.

By Minghao Xiao

rag

arXiv AI

Jun 12

Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices

arXiv:2606. 12742v1 Announce Type: new Abstract: Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector.

By Farough Shayeste Roodi, Parham Zilouchian Moghaddam, Mahdi Mohammadi-nasab, Mehdi Modarressi, Mostafa Ersali Salehi Nasab, Masoud Daneshtalab

efficiency benchmarks

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