Abstract
Continual neural signals recording is very important in the design of an effective brain machine interface and also to interpret human neurophysiology. Advancements in technology made the electronics to be capable of recording signals from large number of neurons on a single device. The demand for data from large number of neurons is continuously increasing from day to day. It is required for near approximate estimation of a challenging tool for the design engineers to produce an efficient Neural Recording Front End (NRFE). For small implant size, area occupied per channel must be low. Dynamic range in NRFE varies with respect to time due to change in the distance between electrode and neuron or background noise which requires adaptability. In this work, techniques for reduction of power consumption per channel and reduction in area consumption per channel in a NRFE are studied, via new circuits and architectures, and compared for proper choice of sub-blocks.
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Brundavani, P., Vishnu Vardhan, D. (2020). Review of Low Power Techniques for Neural Recording Applications. In: Gunjan, V., Zurada, J., Raman, B., Gangadharan, G. (eds) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 885 . Springer, Cham. https://doi.org/10.1007/978-3-030-38445-6_14
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DOI: https://doi.org/10.1007/978-3-030-38445-6_14
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