A Human–Computer-Interaction Exploration of Working Memory Using EEG

In the context of Electroencephalography (EEG) research, how is Working Memory (WM) leveraged in Human-Computer Interaction (HCI)? To address this question, this paper explores how WM is represented in EEG-based HCI experiments, with the aim of informing interface and system design that more effectively aligns with users’ cognitive capacities and limitations. A total of 132 studies published between 2018 and 2024 were reviewed to identify HCI use-cases involving EEG to study WM, outline key WM concepts, and evaluate how these align with findings from other disciplines. The findings indicate that WM-related EEG studies in HCI aim to enhance user interaction through more efficient signal analysis and the development of adaptive systems and brain-computer interfaces (BCIs). However, the findings also highlight the lack of theoretical grounding for EEG-based WM research within HCI. Key cognitive theories are often overlooked, and the strong association between WM and Attention is rarely acknowledged. Although the neural basis of Working Memory (WM) is well represented, its conceptualization within HCI remains underdeveloped and often misaligned with advances in cognitive science and psychology—potentially limiting the development of safe, effective, and cognitively-aware user-centered technologies. Based on these findings, we recommend: (a) integrating alternative models of Working Memory (WM) into HCI system design; (b) incorporating Attention evaluation in EEG-based WM experiments; and (c) exploring the development of a custom WM foundation model to address conceptual limitations in HCI and variability across tasks, users, and environments.