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Recent technological advances in brain recording and artificial intelligenceare propelling a new paradigm in neuroscience beyond the traditional controlledexperiment. Rather than focusing on cued, repeated trials, naturalisticneuroscience studies neural processes underlying spontaneous behaviorsperformed in unconstrained settings. However, analyzing such unstructured datalacking a priori experimental design remains a significant challenge,especially when the data is multi-modal and long-term. Here we describe anautomated approach for analyzing simultaneously recorded long-term,naturalistic electrocorticography (ECoG) and naturalistic behavior video data.We take a behavior-first approach to analyzing the long-term recordings. Usinga combination of computer vision, discrete latent-variable modeling, and stringpattern-matching on the behavioral video data, we find and annotate spontaneoushuman upper-limb movement events. We show results from our approach applied todata collected for 12 human subjects over 7--9 days for each subject. Ourpipeline discovers and annotates over 40,000 instances of naturalistic humanupper-limb movement events in the behavioral videos. Analysis of thesimultaneously recorded brain data reveals neural signatures of movement thatcorroborate prior findings from traditional controlled experiments. We alsoprototype a decoder for a movement initiation detection task to demonstrate theefficacy of our pipeline as a source of training data for brain-computerinterfacing applications. Our work addresses the unique data analysischallenges in studying naturalistic human behaviors, and contributes methodsthat may generalize to other neural recording modalities beyond ECoG. Wepublicly release our curated dataset, providing a resource to studynaturalistic neural and behavioral variability at a scale not previouslyavailable.

Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings