Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism
This work addresses the need for more trustworthy interpretability in clinical applications of deep learning for neuro-developmental disorders like ASD, though it appears incremental in improving existing methods.
The study tackled the problem of unreliable interpretability in deep learning for EEG-based emotion recognition, particularly in Autism Spectrum Disorder (ASD), by evaluating a CNN with a novel ROAR methodology to recover relevant features, achieving successful emotion recognition in both typically-developed and ASD individuals.
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.