A low bandwidth pulse-based neural recording system

This research tests for the first time in-vivo a data reduction scheme based on a modified integrate-and-fire pulse encoding for an implanted neural recording system in wireless transmission applications. Wireless transmission from implanted multi-channel recordings imposes many constraints on the system but the major constraint is bandwidth. Other constraints such as large dynamic range, low power consumption, small device size and noise robustness, are serious but can be more easily met.;This neural recording system consists of a front-end hardware recording part and a back-end signal processing part. The integrate-and-fire (IF) mechanism is adopted in the analog front-end circuit design to achieve low bandwidth for data compression. The neural signal is encoded and transformed into a pulse representation. The encoded pulse representation is inherently noise robust and beneficial in wireless transmission for the signal processing in the digital back-end system. As a result, a traditional analog-to-digital converter (ADC), is not required in this neural recording application. In the digital back-end part, the system can either reconstruct the recorded pulses back to a traditional sampled continuous-time signal, and then sort neural spikes upon the reconstructed signals, or directly execute the pulse-based spike sorting algorithm in the pulse domain, even when the maximum inter-pulse interval (IPI) of the encoded pulses is in sub-Nyquist regions.;In this research, we first successfully record action potentials via the UF system adopting the IF neuron circuit in the in-vivo recording. To conduct an in-vivo recording, the implanted electrode must be well placed to detect available neural signals, and the analog and digital parts of the UF system need parameter optimization and calibration. In addition, the dual system experiment, comprising the UF system and the TDT system, verifies that the UF recording system extracts quality in-vivo signals. In an in-vivo recording, the spike sorting results for these recording systems classify the same neural signals. The UF system can record 1000 muVpp high action potential signals but induces about 3.6 dB higher noise levels than the TDT recording system does. The SNR of the UF system is about 11.43 dB with a pulse rate less than 30 Kpulses/sec while the SNR of the TDT system ranges is about 15.03 dB with a bandwidth of 400 Kbits/sec. The trade-off of SNR and recording bandwidth is observed. Although the sorted spikes in the reconstructed signal are distorted, the distortion is constant throughout the recording and the error does not influence the neural signal classification. This experiment shows that the decrease of the SNR does not influence the spike sorting result.;The modified UF system can reduce the wireless transmission bandwidth via three versatile neuron circuit strategies: the adaptive, leaky and refractory components of the neuron circuit form the adaptive leaky refractory integrate-and-fire (ALRIF) neuron circuit. MATLAB simulation results for all these neuron circuit models show a proof of concept. The refractory neuron circuit limits the maximum peak data bandwidth. The leaky neuron circuit filters out high-frequency noise, which further reduces bandwidth. The adaptive neuron circuit achieves more than 40% data compression compared to the simple IF neuron circuit in simulation. The idea of the adaptive neuron circuit is novel in the integration with a simple IF neuron circuit and unique for reconstruction purposes. The design, fabrication and test, of the adaptive neuron circuit are presented. Simulation of the complete ALRIF neuron circuit illustrates its performance by showing three distinctly sorted spikes in a neural simulator test.