Explainable Artificial Intelligence (XAI) for EEG Analysis: A Survey on Recent Trends and Advancements

Recent advancements in XAI have radically changed the way that AI systems are evaluated, as transparency and trustworthiness are now valued as highly as performance. This is especially true in medical applications, as, in order for such tools to be used in practical applications, interpretability is a key requirement for clinical adoption. Electroencephalography (EEG) analysis, in particular, has seen a significant rise in research, as the difficult and complex nature of EEG signals benefits from these methods, enabling researchers and practitioners to gain new insights from the vast amount of data that is now available. This survey presents a comprehensive analysis of the latest trends and advancements in XAI for EEG analysis. First, we provide a brief overview of fundamental EEG tasks, available datasets, and AI model approaches used for analysis. Then, we classify XAI methods using well-established taxonomies in XAI research, such as locality and generalization of explanations. By exploring all relevant XAI techniques in EEG analysis, our study offers researchers a clear perspective on the current state of the field and identifies potential research gaps. Our review indicates that current XAI approaches for EEG often face limitations in robustness, consistency, and neuroscientific grounding. These findings highlight the need for more reliable and domain-informed explainability methods to support trustworthy EEG analysis in research and clinical practice.