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

Abstract

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 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.

References in corpus (13)

• Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
• Deep learning tools for the measurement of animal behavior in neuroscience
• Deep Learning is Robust to Massive Label Noise
• Predictability and hierarchy in Drosophila behavior
• Composing graphical models with neural networks for structured representations and fast inference
• MoVi: A Large Multipurpose Motion and Video Dataset
• EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
• Movement science needs different pose tracking algorithms
• Learning from Noisy Labels with Distillation
• Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
• Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
• Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
• Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels