Abstract
Surface-recorded somatosensory-evoked potentials (SEPs) are neural signals elicited by an external stimulus. In the case of electrically induced SEPs, the artifact generated by the stimulation process can severely distort the signal. In some cases, the artifact tail often lasts well into the initiation of the SEP making the determination of absolute latency very difficult. In this work, a new approach was taken to identify factors that affect the tail of the artifact. The methodology adopted was the development of a lumped electrical circuit model of the artifact generation process. While the modeling of the instrumentation hardware is relatively simple, this is not the case with tissue and electrode/skin interface effects. Consequently, this paper describes a novel tissue modeling approach that uses an autoregressive moving average (ARMA) parametric technique and an artificial neural network (ANN) to estimate tissue parameters from experimental data. This coupled with an estimation of the stimulation electrode-skin impedance completes the lumped circuit model. Simulink (The Mathworks Inc.) was used to evaluate the model under several different conditions. These results show that both the stimulation electrode-skin interface impedance and nature of the body tissue directly under the recording electrodes have a profound effect on the appearance of the stimulus artifact tail. This was verified by experimentally recorded data obtained from the median nerve using surface electrodes. Conclusions drawn from this work include that stimulation electrodes with low series capacitance should be used whenever possible to minimize the duration of the artifact tail.