The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is development of a new neuroelectric sensor (NeuroDot) for brain monitoring that will measure electric fields generated by brain activity using a new technique termed Electric field Encephalography (EFEG) with best-in-class sensitivity. The EFEG brain monitoring technique is a new modality that has several advantages over the current modalities, electroencephalography EEG (which measures the electric potential on the scalp) and magnetoencephalography MEG (which measures the magnetic field). The NeuroDot brain sensors are quickly mountable and demountable and enable 24/7 neuromonitoring with wireless connectivity to smartphones and wearable technologies like smartwatches. The NeuroDot sensors provide neurological data analyzed with subsequent software post-processing to provide information on the activity of the brain for analysis of the mental health state. Some of the immediate areas of impact of the EFEG technology based NeuroDot sensors include functional brain imaging at high temporal and spatial resolution to enable localization of epileptic seizures, and understanding of pattern recognition and cognition by the brain. The signals from these sensors will provide insights into mental conditions related to aging, sleep, epilepsy and neurological disorders. The high performance NeuroDot sensors will also be usable for human-machine and brain-computer interfaces.
This proposed project will improve and optimize the current NeuroDot prototype to create marketable products that meet specifications for real-time neuromonitoring. This project will achieve its aims through the following Goals. Goal 1: Optimize the wireless NeuroDot sensor to meet metrics and milestones for real-time neuromonitoring. Goal 2: Perform Usability Testing and Sensor Performance Benchmarking through studies on human subjects. The project will develop breakthrough nanodendritic electrodes and wireless technologies for the NeuroDot sensor for long-term low noise EFEG recordings in a compact (< 1square cm) form factor. The critical research tasks involve the characterization of the sensor performance including standard metrics pertaining to sensitivity and noise immunity - signal-to-noise ratio (SNR) and common-mode rejection ratio (CMMR) - as well as robustness to common artifacts afflicting other neuroelectric measurement modalities (e.g., electrode movement, jaw and ocular movements, baseline drift). Specific benchmarks of NeuroDot performance in terms of signal-to-noise ratio, wireless connectivity, battery life and signal quality will be assessed. The human subjects tests will provide benchmarks for neuromonitoring performance in terms of parameters like information bit rate, speed of stimulus response, measurement of visually evoked potential, spectral content of brain activity and classification accuracy.
