Innovative Microelectronic Signal Processing Techniques for the Recording and Analysis of the Human Electroneurogram

Student thesis: Doctoral ThesisPhD


Injuries involving the nervous system are among the most devastating and life altering of all neurological disorders. The resulting loss of sensation and voluntary muscle control represent a drastic change in the individuals lifestyle and independence. Spinal cord injury affects over two hundred thousand people within the United States alone. While there have been many attempts to develop neural interfaces that can be used as part of a prosthetic device to improve the quality of life of such patients and contribute to the reduction of ongoing health care costs, the design of such a device has proved elusive. Direct access to the spinal cord requires potentially life threatening surgery during which the dura, the protective covering surrounding the cord, must be opened with a resulting high risk of infection. For this reason research has been focussed on the stimulation of and recording from the peripheral nerves in an attempt to restore the functionality that has been lost through spinal cord injury. This thesis is concerned with the current status and limitations of peripheral nerve interfaces that are designed for recording electrical signals directly from the nervous system using a technique called velocity selective recording. This technique exploits the relationship between axonal diameter, which is linked via anatomy to function, and the speed with which the axon conducts excitation. New techniques are developed that improve current methods for identifying and simulating neural signals and power efficient implementations of these methods are presented in modern microelectronic platforms. Results are presented from pioneering experiments in rat and pig that for the first time demonstrate the recording and analysis of the physiological electroneurogram using velocity based methods. New methods are developed that enable the extraction of neuronal firing rates and thus the extraction of the information encoded within the nervous system.
Date of Award12 Feb 2016
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorChristopher Clarke (Supervisor) & John Taylor (Supervisor)


  • Neural Interface
  • Neural Recording
  • Velocity Selective Recording
  • Bioelectronics

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