{"total":91,"rows":[{"id":"2412.07236","title":"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding","created":"2024-12-10","cited_by_count":199,"area":null},{"id":"2105.11043","title":"SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification","created":"2021-05-23","cited_by_count":84,"area":null},{"id":"2111.08446","title":"Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions","created":"2021-11-03","cited_by_count":43,"area":null},{"id":"2405.17766","title":"SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals","created":"2024-05-28","cited_by_count":41,"area":null},{"id":"2109.01824","title":"Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification","created":"2021-09-04","cited_by_count":37,"area":null},{"id":"2105.13864","title":"SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging","created":"2021-05-24","cited_by_count":32,"area":null},{"id":"2101.02452","title":"RobustSleepNet: Transfer learning for automated sleep staging at scale","created":"2021-01-07","cited_by_count":24,"area":null},{"id":"2301.03441","title":"L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging","created":"2023-01-09","cited_by_count":21,"area":null},{"id":"2407.20254","title":"EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification","created":"2024-07-20","cited_by_count":21,"area":"ssm"},{"id":"2107.04470","title":"ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training","created":"2021-07-09","cited_by_count":17,"area":null},{"id":"2401.05363","title":"Generalizable Sleep Staging via Multi-Level Domain Alignment","created":"2023-12-13","cited_by_count":17,"area":null},{"id":"2105.12916","title":"Robust learning from corrupted EEG with dynamic spatial filtering","created":"2021-05-27","cited_by_count":14,"area":null},{"id":"2210.06286","title":"Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation","created":"2022-10-10","cited_by_count":14,"area":null},{"id":"2208.06991","title":"Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers","created":"2022-08-15","cited_by_count":13,"area":null},{"id":"2209.11173","title":"U-Sleep's resilience to AASM guidelines","created":"2022-09-19","cited_by_count":12,"area":null},{"id":"2112.03379","title":"Deep Efficient Continuous Manifold Learning for Time Series Modeling","created":"2021-12-03","cited_by_count":10,"area":null},{"id":"2304.06485","title":"CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities","created":"2023-03-27","cited_by_count":10,"area":null},{"id":"2601.06134","title":"DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI","created":"2026-01-05","cited_by_count":9,"area":null},{"id":"2203.12590","title":"TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging","created":"2022-03-22","cited_by_count":8,"area":null},{"id":"2204.03173","title":"Automated Sleep Staging via Parallel Frequency-Cut Attention","created":"2022-04-07","cited_by_count":7,"area":null},{"id":"2209.09452","title":"SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning","created":"2022-09-20","cited_by_count":7,"area":null},{"id":"2310.06715","title":"S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models","created":"2023-10-10","cited_by_count":7,"area":null},{"id":"2605.14698","title":"NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces","created":"2026-05-14","cited_by_count":7,"area":null},{"id":"2110.09966","title":"SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging","created":"2021-10-15","cited_by_count":6,"area":null},{"id":"2211.15374","title":"Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model","created":"2022-11-23","cited_by_count":6,"area":null},{"id":"2311.07788","title":"CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion","created":"2023-11-13","cited_by_count":6,"area":null},{"id":"2401.05411","title":"RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG","created":"2023-12-26","cited_by_count":6,"area":null},{"id":"2101.02530","title":"MSED: a multi-modal sleep event detection model for clinical sleep analysis","created":"2021-01-07","cited_by_count":5,"area":null},{"id":"2209.11007","title":"Avoiding Post-Processing with Event-Based Detection in Biomedical Signals","created":"2022-09-22","cited_by_count":5,"area":null},{"id":"2209.13645","title":"PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition","created":"2022-09-26","cited_by_count":5,"area":null},{"id":"2212.04881","title":"ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation","created":"2022-12-09","cited_by_count":5,"area":null},{"id":"2302.10182","title":"PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2411.04644","title":"wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals","created":"2024-11-07","cited_by_count":5,"area":null},{"id":"2207.01910","title":"Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2207.04585","title":"A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture","created":"2022-07-11","cited_by_count":4,"area":null},{"id":"2602.13857","title":"sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals","created":"2026-02-14","cited_by_count":4,"area":null},{"id":"2102.03352","title":"Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals","created":"2021-01-29","cited_by_count":3,"area":null},{"id":"2410.11200","title":"SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning","created":"2024-10-15","cited_by_count":3,"area":null},{"id":"2411.00128","title":"Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations","created":"2024-10-31","cited_by_count":3,"area":null},{"id":"2502.17213","title":"Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics","created":"2025-02-24","cited_by_count":3,"area":null},{"id":"2510.27522","title":"Leveraging Generic Time Series Foundation Models for EEG Classification","created":"2025-10-31","cited_by_count":3,"area":null},{"id":"2108.10211","title":"Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods","created":"2021-08-23","cited_by_count":2,"area":null},{"id":"2111.10245","title":"Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous Sensing","created":"2021-11-19","cited_by_count":2,"area":null},{"id":"2207.13801","title":"Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning","created":"2022-07-27","cited_by_count":2,"area":null},{"id":"2402.17779","title":"Assessing the importance of long-range correlations for deep-learning-based sleep staging","created":"2024-02-22","cited_by_count":2,"area":null},{"id":"2404.15342","title":"WaveSleepNet: An Interpretable Network for Expert-like Sleep Staging","created":"2024-04-11","cited_by_count":2,"area":null},{"id":"2406.16911","title":"Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models","created":"2024-06-06","cited_by_count":2,"area":null},{"id":"2503.04582","title":"PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2602.07628","title":"SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures","created":"2026-02-07","cited_by_count":2,"area":null},{"id":"2201.00644","title":"Feature matching as improved transfer learning technique for wearable EEG","created":"2021-12-29","cited_by_count":1,"area":null},{"id":"2205.11680","title":"HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records","created":"2022-05-24","cited_by_count":1,"area":null},{"id":"2209.11174","title":"SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features","created":"2022-09-21","cited_by_count":1,"area":null},{"id":"2304.06033","title":"Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models","created":"2023-03-28","cited_by_count":1,"area":null},{"id":"2305.09543","title":"EEG-based Sleep Staging with Hybrid Attention","created":"2023-05-16","cited_by_count":1,"area":null},{"id":"2309.07156","title":"Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability","created":"2023-09-10","cited_by_count":1,"area":null},{"id":"2312.03196","title":"Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging","created":"2023-12-06","cited_by_count":1,"area":null},{"id":"2501.03764","title":"SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment","created":"2025-01-07","cited_by_count":1,"area":null},{"id":"2502.16049","title":"Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2510.07524","title":"EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2511.03488","title":"NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2605.09905","title":"Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2606.08583","title":"A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning","created":"2026-06-07","cited_by_count":1,"area":null},{"id":"2607.21402","title":"MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning","created":"2026-07-23","cited_by_count":1,"area":null},{"id":"2105.11452","title":"Neural Network Based Sleep Phases Classification for Resource Constraint Environments","created":"2021-05-25","cited_by_count":0,"area":null},{"id":"2211.03282","title":"Performance and utility trade-off in interpretable sleep staging","created":"2022-11-07","cited_by_count":0,"area":null},{"id":"2302.12709","title":"Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage","created":"2023-02-17","cited_by_count":0,"area":null},{"id":"2306.04663","title":"U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2404.06869","title":"SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography","created":"2024-04-10","cited_by_count":0,"area":null},{"id":"2408.12792","title":"Boundary Density Likelihood for Direct Event-Time Supervision","created":"2024-08-23","cited_by_count":0,"area":null},{"id":"2409.13367","title":"ALPEC: A Comprehensive Evaluation Framework and Dataset for Machine Learning-Based Arousal Detection in Clinical Practice","created":"2024-09-20","cited_by_count":0,"area":null},{"id":"2410.19819","title":"Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2501.16329","title":"sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging","created":"2025-01-27","cited_by_count":0,"area":null},{"id":"2502.17470","title":"MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification","created":"2025-02-13","cited_by_count":0,"area":null},{"id":"2504.13229","title":"PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning","created":"2025-04-17","cited_by_count":0,"area":null},{"id":"2505.17640","title":"A Network Science Approach to Granular Time Series Segmentation","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2506.11238","title":"uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2509.02982","title":"StableSleep: Source-Free Test-Time Adaptation for Sleep Staging with Lightweight Safety Rails","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2509.06974","title":"Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2510.17406","title":"Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2512.22976","title":"A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging","created":"2025-12-28","cited_by_count":0,"area":null},{"id":"2601.00014","title":"Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI","created":"2025-12-20","cited_by_count":0,"area":null},{"id":"2601.04057","title":"Using Legacy Polysomnography Data to Train a Radar System to Quantify Sleep in Older Adults and People living with Dementia","created":"2026-01-07","cited_by_count":0,"area":null},{"id":"2603.26738","title":"SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging","created":"2026-03-22","cited_by_count":0,"area":"mllm"},{"id":"2606.13694","title":"Efficient Temporal Modeling for Mobile Sleep Staging via Lightweight Random Attention","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2607.04934","title":"Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets","created":"2026-07-06","cited_by_count":0,"area":"ssm"},{"id":"2607.23284","title":"StageGuard: Physiologically Constrained Sleep Staging","created":"2026-07-25","cited_by_count":0,"area":null},{"id":"2607.25197","title":"LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2608.18571","title":"NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.15740","title":"A Language-Guided Multimodal Foundation Model for Zero-Shot and Multi-Task Brain Signal Analysis","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2609.19062","title":"LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2610.03911","title":"COVER: Learning to Accept More in Selective Sleep Staging","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}