Abstract
The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain–machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.
Highlights
In the past decade, researchers have worked on the brain to understand its functions and monitor the brain’s electrical signals to research, diagnose and treat its disorders, as well as to utilize these signals to control artificial limbs
We briefly explain the necessity of the neural recording, especially the invasive method of implanting a chip on the brain in the skull
We review the various architectures of neural recording systems and conclude that architecture that utilizes an analog to digital converters (ADCs) for each column is the best option for the very large-scale recording
Summary
Researchers have worked on the brain to understand its functions and monitor the brain’s electrical signals to research, diagnose and treat its disorders, as well as to utilize these signals to control artificial limbs. The main reason is due to the short-channel effects of MOS transistors These effects in the MOS down-scaled technologies decrease the transconductance (gm ) of the transistor on one hand and on the other hand increase the gate leakage current, the flicker, and thermal noise power of an MOS transistor. This creates challenges in the design of the high gain and low noise neural amplifier, which will be explained in this paper. They must be compact, high gain, low power, and low noise amplifiers To satisfy these constraints in the design of the neural amplifiers, various topologies and techniques are proposed which are presented in the subsections of Section 4.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
