Sensor Artifact and Noise Reduction Algorithms for Cognitive and Physiological Status Monitoring

Cognitive and physiological monitoring of warfighters in training and fielded operations requires robust ambulatory electrophysiological recording systems. Recordings in military operational environments are subject to numerous types of noise and artifact that contaminate the signal of interest. These artifacts can be attributed to external interfering sources, cross-contaminating physiologic signals, or be the consequence of significant subject motion. The intensity of this interference can overwhelm standard recording and noise and artifact reduction techniques, compromising the quality of data. In this SBIR research project, Quantum Applied Science & Research, (QUASAR), Inc. aims to build upon its Phase I work to develop an intelligent Sensor Artifact and Noise Reduction Algorithms (SANoRA) system. This system will consists of advanced multimodal biosensors integrated with computational algorithms for noise and artifact reduction. SANoRA incorporates prior knowledge of artifacts with auxiliary sensor information and physiological data, to (a) automatically detect the presence of artifacts, (b) classify these using a taxonomy of known types, and (c) apply appropriate reduction algorithms. The benefits of SANoRA include improvement in the accuracy of cognitive and physiological status monitoring under mobile conditions, through real time mitigation of a wide spectrum of interference present in operational military environments