Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning

arXiv:2605.06724 · cs.LG, cs.AI, eess.SP · Submitted 2026-05-07 · Read on arXiv

cs.LG, cs.AI, eess.SP

Submitted: 2026-05-07

Updated: 2026-09-26

Terminology

• Fidelity — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Wavelet Transform — 7× in this paper · explained in 2 episodes
• Signal-to-Noise Ratio — 6× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Empirical Mode Decomposition — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Peak Signal-to-Noise Ratio — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Signal-to-noise ratio (SNR) — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Intelligent Partitioning for Self-supervised Denoising — 1× in this paper
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 1× in this paper
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 5 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• White Gaussian Noise — 1× in this paper

Sources

• Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localisation Using Reinforcement Learning
• Proximal Policy Optimization Algorithms

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks