1 research outputs found
Electromyogram Interference Reduction In Neural Signal Recording Using Simple RC Compensation Circuits
Neuroprosthesis can partially restore lost motor functionalities of
individuals such as bladder voiding using functional electrical stimulation (FES)
techniques. FES involves applying pattern of electrical current pulses using
implanted electrodes to trigger affected nerves that are damaged due to
paralysis. A neural signal recorded using tripolar cuff electrodes is significantly
contaminated due to the presence of EMG interference from the surrounding
muscles. Conventional neural amplifiers are unable to remove such interferences
and modifications to the design are required. The modification to the design of
the Quasi-tripole (QT) amplifier is considered in this work to minimise the EMG
interferences from neural signal recording. The analogy between this modified
version of QT known as mQT and Wheatstone bridge claims to neutralise the
EMG interference by adding compensation circuit to either end of the outer
electrodes of the tripolar cuff and therefore balancing the bridge. In this work, we
present simple 3 and 2 stage RC compensation circuits to minimise EMG
interference in trying to balance the bridge in the neural frequency band of interest
(500-10kHz). It is shown that simple RC compensation circuit in series reduces
EMG interference only at the spot frequency rather than linearly in the entire
frequency band of interest. However, two and three stages RC ladder
compensation circuits mimicking electrode-electrolyte interface, can minimize the
EMG interference linearly in the entire frequency band of interest, without
requiring any readjustment to their components. The aim is to minimise EMG
interference as close to null as possible. Invitro testing of about 20% imbalanced
cuff electrode with proposed 3 and 2 stage RC ladder compensation circuits
resulted in linear EMG interference reduction atleast by a factor of 6. On an
average, this yielded an improvement of above 80% EMG minimisation, in
contrast to above 90% observed in the optimisation results, when 1Ω
transimpedance (EMG) was introduced into the setup. Further improvements to
the setup and design can give more promising results in reliable neural signal
recording for FES applications