Spatial Redundancy Reduction in Multi-Channel Implantable Neural Recording Microsystems.

This paper introduces a lossless approach for data reduction in multi-channel neural recording microsystems. The proposed approach benefits from eliminating the redundancy that exists in the signals recorded from the same space in the brain, e.g., local field potentials in intra-cortical recording from neighboring recording sites. In this approach, a single baseline component is extracted from the original neural signals, which is treated as the component all the channels share in common. What remains is a set of channel-specific difference components, which are much smaller in word length compared to the sample size of the original neural signals. To make the proposed approach more efficient in data reduction, length of the difference component words is adaptively determined according to their instantaneous amplitudes. This approach is low in both computational and hardware complexity, which introduces it as an attractive suggestion for high-density neural recording brain implants. Applied on multi-channel neural signals intra-cortically recorded using 16 multi-electrode array, the data is reduced by around 48%. Designed in TSMC 130-nm standard CMOS technology, hardware implementation of this technique for 16 parallel channels occupies a silicon area of 0.06 mm, and dissipates 6.4 μW of power per channel when operates at V=1.2V and 400 kHz.Clinical Relevance- This paper presents a lossless data reduction technique, dedicated to brain-implantable neural recording devices. Such devices are developed for clinical applications such as the treatment of epilepsy, neuro-prostheses, and brain-machine interfacing for therapeutic purposes.