Current models on Explainable Artificial Intelligence (XAI) have shown an evident and quantified lack of reliability for measuring feature-relevance when statistically entangled features are proposed for training deep classifiers. There has been an increase in the application of Deep Learning in clinical trials to predict early diagnosis of neuro-developmental disorders, such as Autism Spectrum Disorder (ASD). However, the inclusion of more reliable saliency-maps to obtain more trustworthy and interpretable metrics using neural activity features is still insufficiently mature for practical applications in diagnostics or clinical trials. Moreover, in ASD research the inclusion of deep classifiers that use neural measures to predict viewed facial emotions is relatively unexplored. Therefore, in this study we propose the evaluation of a Convolutional Neural Network (CNN) for electroencephalography (EEG)-based facial emotion recognition decoding complemented with a novel RemOve-And-Retrain (ROAR) methodology to recover highly relevant features used in the classifier. Specifically, we compare well-known relevance maps such as Layer-Wise Relevance Propagation (LRP), PatternNet, Pattern-Attribution, and Smooth-Grad Squared. This study is the first to consolidate a more transparent feature-relevance calculation for a successful EEG-based facial emotion recognition using a within-subject-trained CNN in typically-developed and ASD individuals.

翻译：当前关于可解释人工智能（XAI）的模型表明，当提出统计纠缠特征用于训练深度分类器时，在测量特征相关性方面存在明显且可量化的可靠性缺失。近年来，深度学习在临床试验中用于预测神经发育障碍（如自闭症谱系障碍，ASD）早期诊断的应用有所增加。然而，利用神经活动特征获得更可靠且可解释指标的显著性映射方法，在诊断或临床试验的实际应用中仍不够成熟。此外，在ASD研究中，使用基于神经指标的深度分类器来预测面部表情情绪几乎未被探索。因此，本研究提出评估用于脑电图（EEG）面部情绪识别解码的卷积神经网络（CNN），并辅以新型的“移除并重新训练”（ROAR）方法，以恢复分类器中使用的高度相关特征。具体而言，我们比较了常用的相关性图，如逐层相关性传播（LRP）、PatternNet、Pattern-Attribution和Smooth-Grad Squared。本研究首次在典型发育个体和ASD个体中，基于受试者内训练的CNN，为成功的脑电图面部情绪识别整合了更透明的特征相关性计算。