Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism. (arXiv:2111.13208v2 [eess.SP] UPDATED)

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 …

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