A Low-Noise, Wireless, Frequency-Shaping Neural Recorder (original) (raw)

This paper presents a low-noise, wireless neural recorder that has a frequency dependent amplification to remove electrode offset and attenuate motion artifacts. The recorder has 2.5 GΩ and 50 MΩ input impedance at 20 Hz and 1 kHz for recording local field potentials and extracellular spikes, respectively. To reduce the input-referred noise, we propose a low-noise frontend design with multiple novel noise suppression techniques. To reduce the power consumption, we have integrated an EC-PC spike processor that automatically adjusts the recording bandwidth based on the signal contents. In bench-top measurement, the proposed neural recorder has 2.2 µV input-referred noise integrated from 300 Hz to 8 kHz and consumes 98 µW maximum power. In animal experiments, the output data of the neural signal processor are serialized and connected to a customized WiFi data link with up to 10 Mbps data rate. Through in-vivo experiments, we find the noise generated by the WiFi doesn't prevent brain recordings with microelectrodes and a clear interpretation of the neural signals; however, the noise can mask the weaker neural signals in nerve recordings with epineural electrodes.