Recent advances in neural recording microsystems

Abstract

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

Keywords: brain-computer interfaces; data management; implantable microsystems; inductive link; low-power biotelemetry; multi-channel; neural recording; power scheduling; sensory circuits; ultrawide-band.

Figure 1.

Block diagrams showing different neural recording microsystem architectures. (a) This approach is employing analog TDM and is sharing a fast ADC between channels. (b) This approach is employing digital TDM and is using one ADC per channel. (c) This approach is employing analog TDM and is transferring digitization on the remote station side by using ATC in the implanted part, followed by TDC in the external part.

Figure 2.

(a) Schematic of a capacitive-feedback neural amplifier. (b) Schematic of an active-feedback neural amplifier.

Figure 3.

(a) Current-mirror OTA. (b) Miller OTA. (c) Folded OTA with source degeneration. (d) Telescopic OTA. (e) Self-biased OTA.

Figure 4.

A data reduction scheme employing a bilateral threshold function. In such scheme, action potentials are detected upon threshold crossing and then tagged with their respective time of occurrence.

Figure 5.

Block diagram of a signal detector employing a pre-processor.

Figure 6.

Block diagram of an implantable neural recording microsystem and its multi-carrier wireless interface.