Multireference adaptive noise cancellation applied to somatosensory evoked potentials

Somatosensory Evoked Potentials (SEP's) contain information that is useful in diagnosing various physiological disorders. However, surface measurements of these potentials suffer from very poor Signal-to-Noise ratio (SNR) resulting in imperceptible SEP waveforms. This factor motivates the employment of dedicated signal processing techniques to improve the quality of the waveform. The objective of this research work is to improve the SNR of SEP by eliminating the predominant myoelectric interference. The strategy followed to achieve this goal is to process the SEP signal by MultiReference Adaptive Noise Cancellation (MRANC). A theoretical model for the MRANC is presented and its performance under the influence of various factors is investigated and compared with other signal processing techniques. The performance of the MRANC is then evaluated by processing simulated and in vivo SEP data. It is found that the MRANC gives a significant improvement in the SNR of the SEP.