Nano-Watt Modular Integrated Circuits for Wireless Neural Interface

The ability to continuously record neural signals from behaving animals and humans has been one of the most important goals in neuroscience and neurophysiology. According to sensing locations, the neural potentials could be categorized by the four primary different signals: action potential, local field potentials, electrocorticogram (ECoG) and scalp electroencephalogram (EEG). Generally, there is a trade-off between these approaches; the more invasive the recording technique, the higher the spatial and spectral frequency content. A practical compromise between signal fidelity and invasiveness is highly needed. This can be achieved by epidural or subdural ECoG recording.
In this work, a nano-watt modular neural interface circuit for ECoG neuroprosthetics has been proposed. The main purpose is to optimize power-performance of the neural interface circuits and to provide a modular system solution to expand functionalities of sensing coverage and modalities. To achieve these aims, the proposed neural interface system has introduced the following contributions/innovations as follows: (1) power-noise optimization based on the ECoG signal driven analysis, (2) extreme low-power analog front-ends, (3) Manchester clock-edge modulation-based clock data recovery, (4) power-efficient data compression, (5) integrated stimulator with fully programmable waveform, (6) wireless signal transmission through skin, and (7) modular expandable design.