eXplainable Artificial Intelligence to embed new-generation Artificial Intelligence architectures for brain signal analysis in clinical scenarios

This research project aims to find new, effective, and trustable data-driven approaches to correctly classify human physiological signals, performing classification and outlier detection. The above can effectively support the medical diagnosis of cognitive and psychological disorders such as anxiety, autism, or depression in biomedical applications. Furthermore, the project aims to construct explainable methods to correctly identify and clarify the classification process, to let those methods be integrated into biomedical scenarios.