Analyzing and reconstructing visual stimuli from brain signals effectively advances our understanding of the human visual system. However, EEG signals are complex and contain significant noise, leading to substantial limitations in existing approaches of visual stimuli reconstruction from EEG. These limitations include difficulties in aligning EEG embeddings with fine-grained semantic information and a heavy reliance on additional large-scale datasets for training. To address these challenges, we propose a novel approach called BrainVis. This approach introduces a self-supervised paradigm to learn EEG time-domain features and incorporates frequency-domain features to enhance EEG representations. We also propose a multi-modal alignment method called semantic interpolation to achieve fine-grained semantic reconstruction. Additionally, we employ cascaded diffusion models to reconstruct images. Using only 9.1% of the training data required by previous mask modeling works, our proposed BrainVis outperforms state-of-the-art methods in both semantic fidelity reconstruction and generation quality. The code is available at https://github.com/RomGai/BrainVis.

Population aging is becoming an increasingly important issue around the world. As people live longer, they also tend to suffer from more challenging medical conditions. Currently, there is a lack of a holistic technology-powered solution for providing quality care at affordable cost to patients suffering from co-morbidity. In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson's disease (DPD) co-morbidity, a condition which severely limits a senior's ability to live actively and independently. We investigate useful in-game behaviour markers which can support machine learning-based predictive analytics on seniors' risk of developing DPD co-morbidity.

Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.

The process of arguing is also the process of justifying and explaining. Here, we focus on argumentative explanations in Abstract Bipolar Argumentation. We propose new defence and acceptability semantics, which operates on both attack and support relations, and use them to formalize two types of explanations, concise and strong explanations. We also show how to compute the explanations with Bipolar Dispute Trees.

Currently, much of machine learning is opaque, just like a "black box." However, in order for humans to understand, trust and effectively manage the emerging AI systems, an AI needs to be able to explain its decisions and conclusions. In this paper, I propose an argumentation-based approach to explainable AI, which has the potential to generate more comprehensive explanations than existing approaches.