High-density neural recording system design

Abstract

Implantable medical devices capable of monitoring hundreds to thousands of electrodes have received great attention in biomedical applications for understanding of the brain function and to treat brain diseases such as epilepsy, dystonia, and Parkinson's disease. Non-invasive neural recording modalities such as fMRI and EEGs were widely used since the 1960s, but to acquire better information, invasive modalities gained popularity. Since such invasive neural recording system requires high efficiency and low power operation, they have been implemented as integrated circuits. Many techniques have been developed and applied when designing integrated high-density neural recording architecture for better performance, higher efficiency, and lower power consumption. This paper covers general knowledge of neural signals and frequently used neural recording architectures for monitoring neural activity. For neural recording architecture, various neural recording amplifier structures are covered. In addition, several neural processing techniques, which can optimize the neural recording system, are also discussed.

Keywords: High-density; Neural processing; Neural recording; Neural signal.

Fig. 1

Spatial and temporal resolution of various brain monitoring modalities [–6]

Fig. 2

Yearly trend of a simultaneously recorded neurons and b a number of channels of the neural recording system implemented in integrated circuits

Fig. 3

Recorded waveforms of a typical raw neural signals and b its filtered signals

Fig. 4

Raw neural signal and its calculated multi-unit activity (MUA) signal

Fig. 5

Conventional neural recording systems that consist of electrodes, LNA, multiplexor, ADC, and wireless telemetry

Fig. 6

Conventional differential input stage and current-reuse differential input stage in neural amplifiers

Fig. 7

ADC structures for neural recording systems depending on resolution and sampling rate

Fig. 8

Spike detection algorithm: a threshold crossing and b nonlinear energy operator (NEO)

Fig. 9

Adaptive electrode-selection method with neural scanning for high-density neural recording systems