Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding

2026-09-21

AI Summary

本文针对跨主体EEG信号解码的泛化问题，提出了一种基于动态系数估计的自适应优化器AFOR，通过在线调整梯度状态提高了模型的适应性和准确性。

Abstract

Electroencephalography (EEG) provides non-invasive monitoring of brain activity and is widely used in emotion recognition, motor imagery and sleep staging. Although within-subject decoding has achieved considerable progress, cross-subject generalization remains a central challenge in practical applications. EEG decoders are typically trained with Adam/AdamW under a fixed second-moment decay coefficient, even though cross-subject learning involves low signal-to-noise ratios, subject variability, and gradient nonstationarity. A fixed coefficient implicitly assumes that gradient statistics are homogeneous across layers and time, which can limit model's adaptability to cross-subject EEG signals and degrade generalization. To address these issues, we propose AFOR, a tensor-wise adaptive optimizer that converts the fixed second-moment decay coefficient into a dynamic coefficient estimated online from local gradient state. AFOR combines a Residual-Alignment Signal Scorer (RASS) and an Adaptive Forgetting Controller (AFC). RASS summarizes local gradient residuals and directional agreement into a signal-quality score, and AFC maps this score through self-referential normalization to a bounded per-step decay coefficient, with cumulative-product initialization correction maintaining consistency under time-varying decay. Under a strict cross-subject protocol on three EEG benchmarks that cover three representative fields, AFOR achieves the best average performance among the compared optimizers, improving the mean test accuracy over Adam by 3.00%, 2.07%, and 4.38%, respectively.

Problem

• cross-subject generalization
• EEG decoding
• nonstationary optimization
• gradient nonstationarity
• signal-to-noise ratios

Innovation

• Adaptive Forgetting
• Cross-Subject Generalization
• Dynamic Coefficient
• Gradient Nonstationarity
• Tensor-wise Optimization