An adaptive filter for steady-state evoked responses

Abstract

A 2-weight adaptive filter that determines the amplitude and phase of steady-state evoked potentials is presented. Reference signals are derived from the visual stimulator that are related to corresponding harmonics of the response and the filter weights are adjusted so as to minimize the squared estimation error between the reference and the recorded signal using the recursive least squares (RLS) method. The filter, which acts as an adaptive bandpass filter, is followed by a detector based on the T circ 2 statistic. The performance of the RLS adaptive filter was compared to that of the conventional Discrete Fourier Transform (DFT) and the filtered DFT of Tang and Norcia in a series of simulations with known sinusoids buried in Gaussian noise and in EEG noise. In the simulations, the RLS adaptive filter detected signals at about 3–4 times lower signal to noise ratios than did the DFT. The RLS filter also outperformed the filtered DFT. Qualitatively similar results were obtained with human visual evoked potential recordings. The adaptive RLS filter significantly outperforms both the DFT and filtered DFT and is much simpler to implement than the filtered DFT method of Tang and Norcia.