Partition-based adaptive estimation of single response evoked potentials

We have introduced and analyzed a new class of adaptive nonlinear filters referred to as partition-based linear (Pl) filters. The operation of those filters depends on partitioning the observation space in some fashion. Specifically, we have used here scaler quantization as an example to illustrate the concept of partitioning the observation space. Each partition is then assigned an output based on linear combinations of observed samples in a moving window of finite length N. The filters are shown to exhibit appealing robustness. Simulations include a novel approach to estimating response-to-response variations in evoked potentials (EP), buried in the on-going electroencephalogram (EEG). Unlike the multi-channel filters currently used in EP estimation, the Pl filters do not require a separate electrode to provide a reference signal. In addition, no repetition of the stimulus is needed and the time of the stimulus need not be known