Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary characteristics of EEG signals pose challenges for training neural networks to acquire appropriate knowledge. Inconsistent EEG signals resulting from these non-stationary characteristics can lead to poor performance. Therefore, it is crucial to investigate and address sample inconsistency to ensure robust performance in spontaneous BCIs. In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training. We present a two-stage dominance score estimation technique that compensates for performance degradation caused by sample inconsistencies. Our proposed method utilizes non-parametric estimation to infer sample inconsistency and assigns each sample a dominance score. This score is then aggregated with the loss function during training to modulate the impact of sample inconsistency. Furthermore, we design a curriculum learning approach that gradually increases the influence of inconsistent signals during training to improve overall performance. We evaluate our proposed method using public spontaneous BCI dataset. The experimental results confirm that our findings highlight the importance of addressing sample dominance for achieving robust performance in spontaneous BCIs.

翻译：深度学习在脑机接口领域的脑信号（如脑电图）解码中展现出巨大潜力。然而，脑电图信号的非平稳特性给神经网络训练以获取适当知识带来了挑战。这些非平稳特性导致的不一致脑电图信号可能造成性能下降。因此，研究并解决样本不一致性对保证自发脑机接口的稳健性能至关重要。本研究引入了样本主导性的概念作为脑电图信号不一致性的度量指标，并提出了一种调节其对网络训练影响的实现方法。我们提出了一种两阶段主导性分数估计技术，用于补偿样本不一致性导致的性能退化。该方法采用非参数估计推断样本不一致性，并为每个样本分配主导性分数。该分数在训练过程中与损失函数进行聚合，以调节样本不一致性的影响。此外，我们设计了课程学习策略，在训练过程中逐步增强不一致信号的影响以提升整体性能。通过公开的自发脑机接口数据集验证，实验结果证实了解决样本主导性问题是实现自发脑机接口稳健性能的关键。