SeeMe: Using Automated Facial Tracking to Detect Voluntary Behavior in Brain Injury

Objective: This prospective interventional study introduces "SeeMe," an automated, high-resolution computer vision platform designed to objectively quantify microscopic, auditory command-evoked movements in patients with Traumatic Brain Injury (TBI). Current clinical assessments, such as the Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R), rely on subjective human observation and often fail to detect low-amplitude motor responses, potentially misclassifying up to 25% of patients as unresponsive. Methodology: SeeMe utilizes vector analysis, cross-correlation, and deep neural networks (DNNs) to track individual facial pores and hand movements with sub-millimeter precision (0.5 mm) and high temporal resolution (0.03s). The study will enroll a cohort of 60-80 TBI patients, alongside healthy controls and pharmacologically paralyzed subjects, to validate SeeMe's sensitivity and specificity. Primary Goals: 1. Validation: Compare SeeMe's detection of voluntary motor recovery against gold-standard clinical examinations (CRS-R). 2. Synchronization: Simultaneously record and time-lock electroencephalography (EEG) and electrocorticography (ECoG) with SeeMe-detected movements. 3. Biomarker Identification: Characterize neural signatures (specifically Beta-band oscillations) associated with the return of voluntary behavior. Impact: By providing a real-time, objective measure of motor intention and execution, SeeMe aims to identify "Cognitive-Motor Dissociation" (CMD) ea…

This description comes directly from the study's public registry record.

Sima Mofakham, PhD  ·  631-444-1278  ·  sima.mofakham@stonybrookmedicine.edu

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