Deep learning for decoding EEG signals has gained traction, with many claims to state-of-the-art accuracy. However, despite the convincing benchmark performance, successful translation to real applications is limited. The frequent disconnect between performance on controlled BCI benchmarks and its lack of generalisation to practical settings indicates hidden overfitting problems. We introduce Disentangled Decoding Decomposition (D3), a weakly supervised method for training deep learning models across EEG datasets. By predicting the place in the respective trial sequence from which the input window was sampled, EEG-D3 separates latent components of brain activity, akin to non-linear ICA. We utilise a novel model architecture with fully independent sub-networks for strict interpretability. We outline a feature interpretation paradigm to contrast the component activation profiles on different datasets and inspect the associated temporal and spatial filters. The proposed method reliably separates latent components of brain activity on motor imagery data. Training downstream classifiers on an appropriate subset of these components prevents hidden overfitting caused by task-correlated artefacts, which severely affects end-to-end classifiers. We further exploit the linearly separable latent space for effective few-shot learning on sleep stage classification. The ability to distinguish genuine components of brain activity from spurious features results in models that avoid the hidden overfitting problem and generalise well to real-world applications, while requiring only minimal labelled data. With interest to the neuroscience community, the proposed method gives researchers a tool to separate individual brain processes and potentially even uncover heretofore unknown dynamics.

翻译：基于深度学习的脑电图信号解码方法已受到广泛关注，许多研究声称达到了最先进的精度。然而，尽管基准测试性能令人信服，但成功转化为实际应用的情况仍较为有限。在受控脑机接口基准测试中的优异表现与在实际场景中泛化能力不足之间的频繁脱节，揭示了隐藏的过拟合问题。本文提出解耦解码分解（D3），一种用于跨脑电图数据集训练深度学习模型的弱监督方法。通过预测输入时间窗口在对应试验序列中的采样位置，EEG-D3能够分离大脑活动的潜在成分，其原理类似于非线性独立成分分析。我们采用一种具有完全独立子网络的新型模型架构，以实现严格的模型可解释性。我们构建了特征解释范式，以对比不同数据集上成分激活模式的差异，并分析其对应的时空滤波器。所提方法在运动想象数据上能可靠地分离大脑活动的潜在成分。基于这些成分的适当子集训练下游分类器，可避免由任务相关伪影引起的隐藏过拟合问题——该问题对端到端分类器影响尤为严重。我们进一步利用线性可分离的潜在空间，在睡眠分期分类任务中实现了有效的少样本学习。该方法能够区分大脑活动的真实成分与虚假特征，从而构建出既能规避隐藏过拟合问题、又能良好泛化至实际应用的模型，且仅需少量标注数据。对于神经科学领域的研究者，本方法提供了分离个体大脑处理过程的有效工具，甚至可能揭示迄今未知的神经动力学机制。