Wireless neural recording devices are necessary for Brain Machine Interfaces (BMIs) because implantable devices reduce the risk of infection and provide subjects unrestrained movability. An implanted neural recording system must satisfy stringent constraints of low power, low noise, compact size, limited bandwidth and robustness. This dissertation developed low-noise, low-power, low-bandwidth neural recording systems and investigated the event-based compression methods suitable for implantable neural recording systems. A low-power low-noise variable-gain variable-bandwidth amplifier is presented to boost weak neural signals to the voltage level that can be processed by data encoders. A novel current-mode circuit design approach to implement the event-based asynchronous Integrate-and-Fire (IF) encoder is proposed to decrease power consumption and design complexity. This research shows that the IF encoder in current-mode implementation for data conversion is a promising alternative to conventional voltage-based and synchronous analog-to-digital converter (ADC) based approaches.;The IF has proven to reduce the data rate in neural recording applications, but is sensitive to DC offset and motion artifacts. This research proposes another novel event-based asynchronous time-derivative (TD) encoder to overcome the offset and artifact issues. The TD encoder concentrates its output events in the regions of high change while the IF encoder generates output events in the regions of high magnitude. The reconstruction algorithm can perfectly recover the input signal if certain Nyquist constraints are met. The CMOS implementation is presented and measurement results show that the TD encoder is suitable for implantable neural recording applications. 13
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