Wireless transmission of neural signals using entropy and mutual information compression.

Two of the most critical tasks when designing a portable wireless neural recording system are to limit power consumption and to efficiently use the limited bandwidth. It is known that for most wireless devices the majority of power is consumed by the wireless transmitter and it often represents the bottleneck of the overall design. This paper compares two compression techniques that take advantage of the sparseness of the neural spikes in neural recordings using an information theoretic formalism to enhance the well-established vector quantization (VQ) algorithm.

Wireless transmission of neuronal recordings using a portable real-time discrimination/compression algorithm.

A design challenge of portable wireless neural recording systems is the tradeoff between bandwidth and power consumption. This paper investigates the compression of neuronal recordings in real-time using a novel discriminating Linde-Buzo-Gray algorithm (DLBG) that preserves spike shapes while filtering background noise. The technique is implemented in a low power digital signal processor (DSP) which is capable of wirelessly transmitting raw neuronal recordings.

Neural signal sampling via the low power wireless pico system.

This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.