Wireless Sensor Brain Machine Interfaces for Closedloop Neuroscience Studies Closedloop bidirectional SensorBrainMachine Interface (SBMI) systems with wireless communication capability have shown significant value in neuroscience studies recently. However, most rackmount medical instrumentation currently used in neuroscience research labs only allow experiments in tethered or sedated animals. Customized miniature wireless SBMI devices, which operate reliably on freely behaving animals, are highly desirable. The design of such system places significant challenges in both circuit and system level. This paper analyzes the system requirements and discusses the design strategies, followed by an example of a fully integrated wireless SBMI for augmenting perception. The design paradigm presented in this work can be used in extensive range of neuroscience studies in unrestrained animals.

MultiChannel Neural Recording Implants: A Review 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. Brainxe2x80x93machine 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, multichannel neural recording implants are surveyed. After presenting various neuralsignal 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.