Adaptive quantization of local field potentials for wireless implants in freely moving animals: an open-source neural recording device.

Objective: Modern neuroscience research requires electrophysiological recording of local field potentials (LFPs) in moving animals. Wireless transmission has the advantage of removing the wires between the animal and the recording equipment but is hampered by the large number of data to be sent at a relatively high rate.

Approach: To reduce transmission bandwidth, we propose an encoder/decoder scheme based on adaptive non-uniform quantization. Our algorithm uses the current transmitted codeword to adapt the quantization intervals to changing statistics in LFP signals. It is thus backward adaptive and does not require the sending of side information. The computational complexity is low and similar at the encoder and decoder sides. These features allow for real-time signal recovery and facilitate hardware implementation with low-cost commercial microcontrollers.

Main Results: As proof-of-concept, we developed an open-source neural recording device called NeRD. The NeRD prototype digitally transmits eight channels encoded at 10 kHz with 2 bits per sample. It occupies a volume of 2  ×  2  ×  2 cm and weighs 8 g with a small battery allowing for 2 h 40 min of autonomy. The power dissipation is 59.4 mW for a communication range of 8 m and transmission losses below 0.1%. The small weight and low power consumption offer the possibility of mounting the entire device on the head of a rodent without resorting to a separate head-stage and battery backpack. The NeRD prototype is validated in recording LFPs in freely moving rats at 2 bits per sample while maintaining an acceptable signal-to-noise ratio (>30 dB) over a range of noisy channels.

Significance: Adaptive quantization in neural implants allows for lower transmission bandwidths while retaining high signal fidelity and preserving fundamental frequencies in LFPs.