发表机构
Chongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences; University of Electronic Science and Technology of China; Beijing University of Posts and Telecommunications（中国科学院重庆绿色智能技术研究院; 电子科技大学; 北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对EEG跨受试者解码中梯度非平稳性问题，提出AFOR优化器，通过动态调整二阶矩衰减系数，在三个基准上平均准确率较Adam提升2-4%。
AI 中文摘要
脑电图（EEG）提供对大脑活动的非侵入性监测，并广泛用于情绪识别、运动想象和睡眠分期。尽管受试者内解码已取得可观进展，但跨受试者泛化仍是实际应用中的核心挑战。EEG解码器通常使用Adam/AdamW在固定的二阶矩衰减系数下训练，尽管跨受试者学习涉及低信噪比、受试者变异性和梯度非平稳性。固定系数隐式假设梯度统计量在层和时间上是同质的，这可能限制模型对跨受试者EEG信号的适应性并降低泛化性能。为解决这些问题，我们提出AFOR，一种张量级自适应优化器，将固定的二阶矩衰减系数转换为从局部梯度状态在线估计的动态系数。AFOR结合了残差对齐信号评分器（RASS）和自适应遗忘控制器（AFC）。RASS将局部梯度残差和方向一致性汇总为信号质量分数，AFC通过自参照归一化将该分数映射为有界的每步衰减系数，并采用累积乘积初始化校正以在时变衰减下保持一致性。在覆盖三个代表性领域的三个EEG基准上，采用严格的跨受试者协议，AFOR在比较的优化器中取得了最佳平均性能，相较于Adam，平均测试准确率分别提高了3.00%、2.07%和4.38%。
英文摘要
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.
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