Digital filtering of on-line evoked potentials

Tracking of changes in latency and amplitude of the evoked potential by using adaptive LMS filters and exponential averagers

The adaptive LMS algorithm in combination with exponential averagers are compared to the use of exponential averagers only in tracking latency and amplitude changes in the evoked potential. The estimator is intended for use in applications where neurologic functions are monitored by detecting changes in the evoked potential. Two different structures of the estimator are evaluated and it is found that averaging before filtering is to be preferred. It is shown that the desired signal to the LMS-filter can have a rather low SNR with only mirror influence on the estimator performance. The estimator which combines an LMS-filter and an exponential averager was shown to detect changes in latency faster than the estimator which uses a nonfiltered average. The LMS-filter is shown to exhibit bias in the estimate of the evoked potential due to the fact that response and background spectra has overlapping frequency ranges. The bias seems not to affect the latency estimation while amplitude estimation was clearly affected. Simulations are performed with both white noise and EEG background.

The implementation of an autoregressive model with exogenous input in a single sweep visual evoked potential analysis

Based on a model of signal-noise interaction, we present a method for single-sweep analysis of Visual Evoked Potentials. The EEG is represented as an autoregressive process and the single-sweep VEP as a filtered version of a reference signal taken as the running average of 20 consecutive sweeps. The algorithm for model identification and filtering is an ARX (AutoRegressive with eXogenous input) which provides a fast and efficient solution by means of a least squares approach. The choice of reference signal, as well as the complexity of the model, is also discussed. A further advantage of this approach is parameter reduction: all the single-sweep information is contained in 18 model coefficients and the reference signal.

Real-time reconstruction of evoked potentials using a new two-dimensional filter method

Standard techniques for evoked potential recording extract a stimulus-locked event from accompanying noise by averaging a large number of sequentially obtained responses. This approach is valid only to the extent that the nervous system's electrical response to successive stimuli is identical. The technique is suboptimal for recording unstable evoked potentials which vary with the subject's state and attention. Similarly, standard methods are suboptimal for efficiently analyzing rapid changes such as may be seen in the operating room. We developed an evoked potential recording method that reconstructs the individual evoked responses (or small subaverages of evoked potentials) to a series of stimuli. First, the raw data from an entire series of one to several hundred responses are recorded digitally. Using a frequency domain two-dimensional filter, the data are then filtered twice, once along the data sequence axis for each trial, and again along the cross-trial sequence axis for comparable frequency coefficients in sequential trials. The reconstructed filtered evoked potentials are plotted, with successive responses stacked for easy tracking of component changes.