EAGER: Ultra-low-power, Universal, Multi-rate Bio-signal Transceiver SoC for Medical Diagnosis and Brain-Machine Interface Applications

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research project is to advance the vision of a universal bio-signal acquisition system-on-chip (SoC). Such a system would have the ability to sense a wide variety of biological signals from the human body. The approach is to use a single reconfigurable ultra-low power low-noise amplifier and multi-rate, time-interleaved analog-to-digital converter (ADC) to support multi-channel, multi-type sensor data. This is in contrast to recent research on biological signal acquisition that focuses on custom integrated circuits that target a particular type of biological signal, such as an electrocardiogram (ECG) signal or an encephalogram (EEG) signal. Another goal is to include a low-power radio frequency (RF) transceiver on the SoC to transmit signal data and receive configuration information.With respect to intellectual merit, the project has the potential to overcome technical challenges in the signal conditioning functional block for a bio-sensor SoC. The targeted signal versatility leads to a requirement for a very wide dynamic range for the amplifier front-end. The need to detect signals in very low signal-to-noise conditions also presents a circuit design challenge. Using CMOS to reduce cost prevents the use of some known techniques to address these challenges. The project investigates a reconfigurable low-noise amplifier for this purpose. The approach is multidisciplinary, integrating digital signal processing, analog and mixed-signal integrated circuit design, and medical science.With respect to broader impacts, the proposed research has the potential to provide significant new capabilities for acquiring biological signals and to substantially lower the cost of such systems. If successful, this transformation could have significant impact on applications in diagnostic healthcare, home healthcare, personal fitness, and brain-controlled human-machine interfaces for prosthetic and other devices. One graduate student will be supported by the proposed project.