Somatosensory evoked potential (SSEP) monitoring during spinal surgery has been associated with a 60% reduction in paraplegia. However, subjective interpretation of signals is not always accurate, and a significant learning curve has been demonstrated. Therefore, we propose the development and testing of innovative quantitative signal processing algorithms, focused on the detection of injury. In our Phase I effort, we will use adaptive Fourier analysis of SSEPs to evaluate parametric changes in the signal, and correlate the results with injury. We will further develop a spectral coherence analysis approach that results in an index of injury we call the Linearity Index. The injury detection algorithm will then be validated using an established rat model of graded spinal cord injury. Electrophysiological measurements will be performed by an expert neuroelectrophysiologist. We will then critically examine the problems of false-positives and false-negatives during SSEP monitoring, as well as study timely detection of spinal cord injury at various spinal compression ratios. If we are successful, we envision a Phase II focused on clinical validation of the device, improved noise-immunity, and investigation of human-subject specific alarm threshold criteria. It is our long-term goal to see the device used as a rapid, accurate sentry for detection of spinal cord compromise during high-risk surgeries.