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（BCI）领域的脑信号（如脑电图，EEG）解码中展现出潜力。然而，EEG信号的非平稳特性为训练神经网络获取恰当知识带来了挑战。这些非平稳特性导致的不一致EEG信号可能引发性能不佳。因此，探究并解决样本不一致问题以确保自发BCI的稳健性能至关重要。本研究引入了样本主导性（sample dominance）概念作为EEG信号不一致性的度量指标，并提出了调节其对网络训练影响的方法。我们提出了一种两阶段主导性得分估计技术，以补偿样本不一致造成的性能退化。所提方法利用非参数估计推断样本不一致性，并为每个样本赋予主导性得分。该得分在训练过程中与损失函数聚合，以调节样本不一致性带来的影响。此外，我们设计了一种课程学习方法，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。我们采用公开的自发BCI数据集对所提方法进行评估。实验结果证实，我们的发现强调了解决样本主导性问题对于实现自发BCI稳健性能的重要性。