A Comprehensive Review of Explainable AI in Deep Learning Algorithms for EEG Analysis

While deep learning techniques are nowadays a powerful field to automatically learn and perform accurate EEG data classification in the clinical context, they still lack wide acceptance within the medical and health research community. This lack of trust is associated with the high complexity deep learning algorithms typically have, which contributes to a low level of interpretability of the outcome and predictions. This survey aims to provide a comprehensive discussion of the latest advancements in deep learning models applied to EEG analysis, while also emphasizing research in explainable AI within this domain. It explores commonly used algorithms in EEG analysis, their main application areas, and the insights provided by XAI. Moreover, the survey addresses current limitations, such as the evaluation of XAI methods and the need for clinical validation, as well as ongoing challenges in this field. One critical insight from this review is the relative paucity of clinical evaluations of the rich stock of proposed techniques and methods for AI explainability. By showcasing various applications and breakthroughs in EEG analysis facilitated by XAI, the survey underscores the potential of these technologies to revolutionize neurological diagnosis and treatment, paving the way for wider acceptance and implementation in clinical settings.