arXiv Machine Learning

Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets

arXiv:2607. 04934v1 Announce Type: new Abstract: Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring.

arXiv Machine Learning

Jul 15

AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts

arXiv:2512. 14461v2 Announce Type: replace Abstract: Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care.

By Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon, Stephan Bialonski

benchmarks

arXiv Machine Learning

Jul 2

Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution

arXiv:2508. 11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia.

By Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi

efficiency

arXiv AI

Jul 7

OSF: On Pre-training and Scaling of Sleep Foundation Models

arXiv:2603. 00190v2 Announce Type: replace-cross Abstract: Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and cohorts.

By Zitao Shuai, Zongzhe Xu, David Yang, Wei Wang, Yuzhe Yang

benchmarks

arXiv Machine Learning

Aug 4

SingLEM: Single-Channel Large EEG Model

arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.

By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka

llms

arXiv Machine Learning

Aug 3

Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms

arXiv:2504. 08469v3 Announce Type: replace-cross Abstract: Current methods for detecting artifacts in sleep EEG range from threshold-based algorithms to machine learning approaches, yet applications remain limited for single-channel mobile EEG.

By Khrystyna Semkiv, Jia Zhang, Maria Laura Ferster, Walter Karlen

arXiv Machine Learning

Jul 20

Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals

arXiv:2607. 15477v1 Announce Type: new Abstract: Sleep apnea diagnosis via polysomnography remains resource intensive and relies on time consuming manual data analysis and scoring.

By Shashank Manjunath, Mukesh Cheemakurthi, Aarti Sathyanarayana

llms computer-vision

arXiv AI

5d ago

Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts

arXiv:2608. 12446v1 Announce Type: cross Abstract: Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models against a single reference hypnogram despite known inter-scorer variability.

By Seyyed Ali Hoseini, Javad Baseri, Hamid Saadatfar, Edris Hoseini Gol, AmirHossein Eshghi

arXiv AI

Jun 15

Efficient Temporal Modeling for Mobile Sleep Staging via Lightweight Random Attention

arXiv:2606. 13694v1 Announce Type: cross Abstract: Mobile sleep staging serves as a foundational infrastructure for in-home sleep monitoring and closed-loop modulation.

By Guisong Liu, Pengfei Wei, Jainsong Zhang, Martin Dresler

llms

arXiv AI

Jun 10

Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

arXiv:2606. 10718v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness.

By Xinglong Cui, Dian Gu

llms benchmarks

arXiv Machine Learning

5d ago

EEG Decoding Using CNN and LSTM Network

arXiv:2608. 13285v1 Announce Type: new Abstract: Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by stroke or neurodegenerative disorders.

By Athanasios Karagounis

arXiv AI

Jul 24

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.

By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu

safety

arXiv Machine Learning

Jun 2

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)

llms fine-tuning benchmarks

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