Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advances in deep neural networks (DNNs) have shown promise, owing to their advanced nonlinear modeling capabilities. However, DNN still faces challenge in decoding EEG samples of unseen individuals. To address this, this paper introduces a novel approach by incorporating the conditional identification information of each individual into the neural network, thereby enhancing model representation through the synergistic interaction of EEG and personal traits. We test our model on the WithMe dataset and demonstrated that the inclusion of these identifiers substantially boosts accuracy for both subjects in the training set and unseen subjects. This enhancement suggests promising potential for improving for EEG interpretability and understanding of relevant identification features.

翻译：脑电解码对于揭示人脑机制和推动脑机接口发展至关重要。传统的机器学习算法受限于脑电信号的高噪声水平和固有个体差异，近年来深度神经网络凭借其先进的非线性建模能力展现出应用前景。然而，深度神经网络在解码未见个体的脑电样本时仍面临挑战。为解决该问题，本文提出一种创新方法，通过将每个个体的条件识别信息融入神经网络，借助脑电信号与个体特征的协同交互来增强模型表征能力。我们在WithMe数据集上测试了所提模型，实验表明引入这些标识符显著提升了训练集受试者与未见受试者的解码准确率。这一提升为改善脑电信号可解释性及理解相关识别特征展现了重要潜力。