Methods for improving the signal-to-noise ratio of endogenous-evoked potentials

A stimulus leads to a cortical response (i.e., evoked potential [EP]) which may be recorded from electrodes attached to the scalp. However, background cortical activity, considered as noise (n), is typically of equal or greater magnitude than the response, which is considered as signal (s). This situation leads to the masking of the presence of the electrocortical signal. Two methods are described which enable the enhancement of the signal with respect to the noise. The first method outlined is time-domain averaging. Its relation to Fourier averaging is also presented. Time averaging can lead to an enhancement of the signal with respect to the noise, known as the signal-to-noise ratio (SNR), by a factor of root N; N being the number of trials recorded. However, latency variability (i.e., jitter) present in the signal leads to a decrement in this maximal potential enhancement. The second technique is an adaptive filter method of averaged cross-correlations, developed by Woody (1967), which deals with the variable latency problem. The development of a latency corrected average developed by McGillem and Aunon (1977) is also presented. The final section describes methods for data handling once the electrocortical signal has been enhanced. It is then necessary to describe the EP quantitatively. Principal Components Analysis (PCA) allows for the quantitative compact representation of the evoked potential waveform. The method also allows for the testing of the effects of explanatory variables on the EP.