Optimal digital filters for long-latency components of the event-related brain potential.

A fundamentally important problem for cognitive psychophysiologists is selection of the appropriate off-line digital filter to extract signal from noise in the event-related brain potential (ERP) recorded at the scalp. Investigators in the field typically use a type of finite impulse response (FIR) filter known as moving average or boxcar filter to achieve this end. However, this type of filter can produce significant amplitude diminution and distortion of the shape of the ERP waveform. Thus, there is a need to identify more appropriate filters. In this paper, we compare the performance of another type of FIR filter that, unlike the boxcar filter, is designed with an optimizing algorithm that reduces signal distortion and maximizes signal extraction (referred to here as an optimal FIR filter). We applied several different filters of both types to ERP data containing the P300 component. This comparison revealed that boxcar filters reduced the contribution of high-frequency noise to the ERP but in so doing produced a substantial attenuation of P300 amplitude and, in some cases, substantial distortions of the shape of the waveform, resulting in significant errors in latency estimation. In contrast, the optimal FIR filters preserved P300 amplitude, morphology, and latency and also eliminated high-frequency noise more effectively than did the boxcar filters. The implications of these results for data acquisition and analysis are discussed.

Dynamics of brain electrical activity.

In addition to providing important theoretical insights into chaotic deterministic systems, dynamical systems theory has provided techniques for analyzing experimental data. These methods have been applied to a variety of physical and chemical systems. More recently, biological applications have become important. In this paper, we report applications of one of these techniques, estimation of a signal's correlation dimension, to the characterization of human electroencephalographic (EEG) signals and event-related brain potentials (ERPs). These calculations demonstrate that the magnitude of the technical difficulties encountered when attempting to estimate dimensions from noisy biological signals are substantial. However, these results also suggest that this procedure can provide a partial characterization of changes in cerebral electrical activity associated with changes in cognitive behavior that complements classical analytic procedures.