Adaptive Noise Cancellation of Brainstem Auditory Evoked Potentials using Systolic Arrays

Abstract

Brainstem Auditory Evoked Potentials (BAEP) contain valuable information about the condition of the neural fibers associated with the auditory pathways. Extraction of this information is a difficult task due to contamination by on-going scalp EEG. This thesis reviews the current processing techniques and introduces adaptive noise cancellation (ANC) using systolic arrays as an alternative to existing technology. Q-R decomposition theory is reviewed and an explanation of the mechanics of systolic adaptive noise cancellation (SANC) is presented. A modified Given's rotation algorithm is derived resulting in a saving of up to 2/3 in memory requirements. Real data were collected in the laboratory. Real and simulated data were processed to determine the characteristics and effectiveness of adaptive noise cancellation strategies. Successful ANC of BAEP was performed on simulated data using a number or signal-to-noise ratios (S/N), data sequence lengths, reference signals and filter parameter values. We conclude that systolic arrays are a very powerful and appropriate technique for the extraction or BAEPs. Correlation studies indicated that the pre-stimulus EEG signal is inadequately correlated to the primary signal for successful ANC or BAEP in real data. A multi-channel collection scheme is outlined for future collection or Evoked Potential data. A summary or experimental results is presented to address the problem or data collection and signal processing optimization.