Deep brain drugdelivery control using vagus nerve communications Abstract   Vagus nerve stimulation (VNS) uses electrical impulses applied at the neck in order to mitigate the effects of, for example, epileptic seizures. We propose using VNS to provide data pulses to communicate with a drugdelivery system embedded near the brainstem. We model the generation of a vagus nerve compound action potential (CAP), calculating the signal attenuation and the resulting transmission range. The metabolic cost of CAP transmission in terms of the use of adenosine triphosphate (ATP) is also calculated. The channel capacity for onoff keying (OOK) is computed from the CAP characteristics, the neural refractory period and the level of background neural noise. The resulting low bitrate, unidirectional asynchronous transmission system is analysed for the use of different methods of forward error correction (FEC) to improve biterror rate (BER). We show a proposed data packet structure that could deliver instructions to an embedded drugdelivery system with multiple addressable drug reservoirs. We also analyse the scope for powering the drugdelivery system with energy harvested from cerebrospinal glucose.

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.