The ultimate goal of my research is to pull neuroscience into the real world using naturalistic experiments and brain-computer interface (BCI) engineering. This is a worthwhile venture: success would enhance our understanding of complex neural functions and enable BCIs to detect disordered brain states as they occur and deliver an intervention when it is needed most – a sort of pacemaker for the brain. In pursuing this goal, we can learn a great deal about how the brain processes not just simple, static stimuli, but the immersive natural world in which our brains have evolved. Better yet, we can cultivate a link to the everyday situations – like reading – in which disorders and symptoms manifest, and to the uniquely human experiences – like music – that inspire young researchers.
My approach to this task is based on an interplay between hypothesis-driven naturalistic experiments and exploratory BCI development. Well-controlled studies are an invaluable tool for understanding cognitive processes but, as we have learned from studies of embodied cognition and naturalistic decision-making, limiting research to traditional laboratory paradigms can obscure important aspects of natural human experience. By engineering new BCIs, we can observe naturalistic signals and use machine learning to identify influential neural components that merit more conventional study. The results of these conventional studies, in turn, can better inform machine learning feature selection and priors. This synergistic approach keeps our conventional experiments anchored to ecologically valid scenarios and improves the accuracy and practicality of our BCIs.
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