MedMamba introduces a multi-view SSM architecture with adaptive graph learning that reports state-of-the-art accuracy on five medical time series datasets at linear complexity.
Eegmamba: Bidirectional state space model with mixture of experts for eeg multi-task classification
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
Sleep-only contrastive pretraining improves results on non-sleep EEG and ECG tasks relative to training from scratch and matches or exceeds some specialized models.
A unified benchmark across 12 ERP datasets finds that foundation models and deep learning generally outperform traditional manual features for stimulus classification and disease detection, with specific embedding strategies improving Transformer performance.
citing papers explorer
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MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
MedMamba introduces a multi-view SSM architecture with adaptive graph learning that reports state-of-the-art accuracy on five medical time series datasets at linear complexity.
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Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
Sleep-only contrastive pretraining improves results on non-sleep EEG and ECG tasks relative to training from scratch and matches or exceeds some specialized models.
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Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models
A unified benchmark across 12 ERP datasets finds that foundation models and deep learning generally outperform traditional manual features for stimulus classification and disease detection, with specific embedding strategies improving Transformer performance.