Recent  advances in brain recording technology and artificial intelligence are propelling a new paradigm in neuroscience beyond the traditional controlled experiment. Naturalistic neuroscience studies neural computations associated with spontaneous behaviors performed in unconstrained settings. Analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. Here we describe an automated approach for analyzing large (%DIFDELCMD < \approx250 %%% GB/subject) datasets of simultaneously recorded human electrocorticography (ECoG) and naturalistic behavior video data  for 12 subjects . Our pipeline discovers and annotates thousands of instances of human upper-limb movement events in long-term (7--9 day) naturalistic behavior data using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching . Analysis of the simultaneously recorded brain data uncovers neural signatures of movement that corroborate prior findings from traditional controlled experiments. We also prototype a decoder for a movement initiation detection task to demonstrate the efficacy of our pipeline as a source of training data for brain-computer interfacing applications. We plan to publish our curated dataset, which captures naturalistic neural and behavioral variability at a scale not previously available . We believe this data will enable further research on models of neural function and decoding that incorporate such naturalistic variability and perform more robustly in real-world settings .
Recent technological advances in brain recording and artificial intelligence are propelling a new paradigm in neuroscience beyond the traditional controlled experiment. Rather than focusing on cued, repeated trials, naturalistic neuroscience studies neural processes underlying spontaneous behaviors performed in unconstrained settings. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. Here we describe an automated approach for analyzing %DIFDELCMD < \approx250 %%% simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and naturalistic behavior video data . We take a behavior-first approach to analyzing the long-term recordings. Using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching on the behavioral video data, we find and annotate spontaneous human upper-limb movement events. We show results from our approach applied to data collected for 12 human subjects over 7--9 days for each subject . Our pipeline discovers and annotates over 40,000 instances of naturalistic human upper-limb movement events in the behavioral videos . Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate prior findings from traditional controlled experiments. We also prototype a decoder for a movement initiation detection task to demonstrate the efficacy of our pipeline as a source of training data for brain-computer interfacing applications. Our work addresses the unique data analysis challenges in studying naturalistic human behaviors, and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publicly release our curated dataset, providing a resource to study naturalistic neural and behavioral variability at a scale not previously available .
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technological
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technology
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Naturalistic
Rather than focusing on cued, repeated trials, naturalistic
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computations associated with
processes underlying
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Analyzing
However, analyzing
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large (
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GB/subject) datasets of simultaneously recorded human
simultaneously recorded long-term, naturalistic
A
. We take a behavior-first approach to analyzing the long-term recordings. Using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching on the behavioral video data, we find and annotate spontaneous human upper-limb movement events. We show results from our approach applied to data collected
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subjects
human subjects over 7--9 days for each subject
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thousands of instances of
over 40,000 instances of naturalistic
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long-term (7--9 day) naturalistic behavior data using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching
the behavioral videos
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uncovers
reveals
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We plan to publish
Our work addresses the unique data analysis challenges in studying naturalistic human behaviors, and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publicly release
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which captures
providing a resource to study
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. We believe this data will enable further research on models of neural function and decoding that incorporate such naturalistic variability and perform more robustly in real-world settings