情感与影响：基于EEG情感分类的正则化连续学习的局限性
Affect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classification
AI总结：
本研究发现基于正则化的连续学习方法在EEG情感分类中存在局限性，主要由于稳定性与可塑性之间的权衡问题。
摘要：
在基于EEG的情感分类中，对未见主体的泛化仍是一个挑战，由于存在高 inter-和 intra-主体变异性。连续学习（CL）通过在一系列任务中学习并缓解灾难性遗忘提供了有前途的解决方案。正则化连续学习方法，如弹性权重巩固（EWC）、突触智能（SI）和记忆感知突触（MAS），常被用作EEG连续学习研究中的基线方法，但其适用于此问题的适宜性仍待探索。本研究理论和实证发现，正则化连续学习方法在DREAMER和SEED数据集上进行EEG情感分类时表现有限。我们发现稳定性-可塑性权衡中存在根本性不匹配，其中正则化方法优先减轻灾难性遗忘（反向转移）而非适应新主体（正向转移）。我们在此主体增量序列下调查了这一限制，并观察到：（1）在噪声数据和协变量偏移下，参数重要性的估计启发式方法变得不可靠；（2）这些启发式方法所认为重要参数的梯度往往干扰新主体所需的梯度更新，使优化远离最小值；（3）跨任务积累的重要性值过度约束模型；（4）性能对主体顺序敏感。正向转移在所有方法和数据集上均未显示出显著优于序列微调的统计学意义（p>0.05）。EEG信号的高变异性意味着过去主体对未来的主体提供有限价值。因此，基于正则化的连续学习方法在基于EEG的情感分类中对未见主体的稳健泛化方面受到限制。
英文摘要：
Generalisation to unseen subjects in EEG-based emotion classification remains a challenge due to high inter-and intra-subject variability. Continual learning (CL) poses a promising solution by learning from a sequence of tasks while mitigating catastrophic forgetting. Regularisation-based CL approaches, such as Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), and Memory Aware Synapses (MAS), are commonly used as baselines in EEG-based CL studies, yet their suitability for this problem remains underexplored. This study theoretically and empirically finds that regularisation-based CL methods show limited performance for EEG-based emotion classification on the DREAMER and SEED datasets. We identify a fundamental misalignment in the stability-plasticity trade-off, where regularisation-based methods prioritise mitigating catastrophic forgetting (backward transfer) over adapting to new subjects (forward transfer). We investigate this limitation under subject-incremental sequences and observe that: (1) the heuristics for estimating parameter importance become less reliable under noisy data and covariate shift, (2) gradients on parameters deemed important by these heuristics often interfere with gradient updates required for new subjects, moving optimisation away from the minimum, (3) importance values accumulated across tasks over-constrain the model, and (4) performance is sensitive to subject order. Forward transfer showed no statistically significant improvement over sequential fine-tuning (p > 0.05 across approaches and datasets). The high variability of EEG signals means past subjects provide limited value to future subjects. Regularisation-based continual learning approaches are therefore limited for robust generalisation to unseen subjects in EEG-based emotion classification.
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