Beyond Workload: Paving the Road for the Next Generation of Implicit Prefrontal Cortex Based Brain-Computer Interfaces
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| Pubblicato in: | ProQuest Dissertations and Theses (2025) |
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| Abstract: | The rapidly evolving field of Human-Computer Interaction (HCI) faces a fundamental constraint: the limited bandwidth of information exchange between users and computing systems. One promising approach to increasing this bandwidth is implicit interaction: a paradigm in which applications modify their state based on information gleaned from users, rather than direct input. Within the context of reading such information from human neural signals, this concept is formally recognized as implicit Brain-Computer Interfaces (implicit BCI). My work focuses on implicit BCIs which measure the prefrontal cortex (PFC); early prototypes have successfully leveraged the PFC to approximate mental workload, but much is left to be understood about the full potential of this region. Through three research projects spanning two brain measurement modalities, this dissertation makes targeted contributions to this area of research. With functional Near-Infrared Spectroscopy (fNIRS), I explore two facets of PFC activation demonstrated in functional Magnetic Resonance Imaging (fMRI)-based neuroscience research which are underexplored in applied contexts: episodic memory and brain-network based classification; in the first project, I study the measurable effects of episodic and working memory within the context of using Large Language Models (LLMs), and in the second project I develop a real-time implicit BCI designed to differentiate between different brain networks. The third project benchmarks low-cost EEG in three studies which distinguish brain states based on different factors: quality of moves made during chess playing, workload levels within standard cognitive psychology tasks, and cognitive states during the tasks. For all studies I use Linear Mixed Models (LMM) to observe macro patterns in the data, and machine learning to explore potential for implicit BCI. Results indicate that, in addition to the well-understood concept of measuring singular aspects of consciousness across a gradient (e.g. workload), promising potential exists for leveraging the PFC towards classification across tasks which engage different cognitive processes, both with fNIRS and low-cost EEG. Further, careful consideration of “noise” in implicit BCI introduces a new idea: Human-Sensor-Computer Interaction (HSCI). Taken together, this dissertation provides relevant context to inform the next generation of Human-Sensor-Computer systems, including PFC-based interfaces stretching past workload, and beyond. |
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| ISBN: | 9798315742524 |
| Fonte: | ProQuest Dissertations & Theses Global |