用于单次试验EEG分类的可解释深度神经网络
Interpretable Deep Neural Networks for Single-Trial EEG Classification
摘要：
背景资料：在认知神经科学中，深度神经网络（DNN）解决复杂分类任务的潜力尚未得到充分利用。最大的限制因素是DNN作为臭名昭著的“黑匣子”不能提供对决定背后的神经生理现象的洞察。分层相关传播（LRP）已被引入作为一种新的方法来解释个人的网络决策。新方法：我们首次提出将DNN与LRP应用于EEG数据分析。通过LRP，单次试验DNN决策被转换为热图，表明每个数据点与决策结果的相关性。结果：DNN实现了与CSP-LDA相当的分类精度。在低性能的受试者中，受试者到受试者的训练DNN的转移可以改善结果。单次试验LRP热图揭示了神经生理学上合理的模式，类似于CSP衍生的头皮图。重要的是，虽然CSP模式代表了类的聚合信息，但LRP热图将神经模式精确定位到单个试验中的单个时间点。与现有方法的比较：我们比较了DNN与线性CSP-LDA在与运动-影像BCI相关的两个数据集上的分类性能。结论：我们已经证明，DNN是一个强大的非线性工具，脑电分析。使用LRP，可以达到神经活动的高分辨率评估的新质量。LRP是DNN缺乏可解释性的潜在补救措施，这限制了它们在神经科学应用中的实用性。LRP衍生热图的极端特异性为研究复杂感知或决策相关过程的神经活动开辟了新途径。
英文摘要：
Background: In cognitive neuroscience the potential of Deep Neural Networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise Relevance Propagation (LRP) has been introduced as a novel method to explain individual network decisions. New Method: We propose the application of DNNs with LRP for the first time for EEG data analysis. Through LRP the single-trial DNN decisions are transformed into heatmaps indicating each data point's relevance for the outcome of the decision. Results: DNN achieves classification accuracies comparable to those of CSP-LDA. In subjects with low performance subject-to-subject transfer of trained DNNs can improve the results. The single-trial LRP heatmaps reveal neurophysiologically plausible patterns, resembling CSP-derived scalp maps. Critically, while CSP patterns represent class-wise aggregated information, LRP heatmaps pinpoint neural patterns to single time points in single trials. Comparison with Existing Method(s): We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginery BCI. Conclusion: We have demonstrated that DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of high-resolution assessment of neural activity can be reached. LRP is a potential remedy for the lack of interpretability of DNNs that has limited their utility in neuroscientific applications. The extreme specificity of the LRP-derived heatmaps opens up new avenues for investigating neural activity underlying complex perception or decision-related processes.
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