Human Performance Monitoring 3 Abstract This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWTwas compared to principal components

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWTwas compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation> algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWTnetwork or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. Human Performance Monitoring 4 The efficiency of the DWTis discussed in terms of its decorrelation and energy compaction properties. In addition, the DWTmodels provided evidence that a pattern of low-frequency activity (I to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Introduction Studies have shown that linear regression models may significantly explain and predict human performance from measures of ERPs elicited by stimuli presented in the context of a task (Trejo, Kramer, & Arnold, 1995). These models have used, as predictors, measures such as the amplitude and latency of ERP components (e.g., NI, P300). Other studies have used more comprehensive measures such as factors derived from principal components analysis and discriminant functions (Humphrey, Sirevaag, Kramer, & Mecklinger, 1990). Such models work best when they have been adapted to the individual subject, taking into account the temporal and topographic uniqueness of the ERP. Even then, the models often suffer from a limited ability to generalize to new data. In addition, the cost of developing and adapting such models for individuals is high, requiring many hours of expert analysis and interpretation of ERP waveforms. Neural-network models for ERPs may be an improvement over linear regression models (DasGupta, Hohenberger, Trejo, & Mazzara, Human Performance Monitoring 5 1990; Ryan-Jones & Lewis, 1991). However, when neural network models have been based on traditional ERP measures, such as the sampled ERP time points or the amplitude of ERP components, the improvement in correlation between ERP measures and human performance has been small, typically about ten percent (Venturini, Lytton, & Sejnowski, 1992). Transformations of ERPs prior to neural network analysis, such as the fast Fourier transform (FFT), may improve neural network models (DasGupta, Hohenberger, Trejo, & Kaylani, 1990). However, the FFT is not ideally suited for representing transient signals; it is more appropriate for continuous signals, such as sine waves. The wavelet transform is well suited for analysis of transients with time-varying spectra (Tuteur, 1989; Daubechies, 1990, 1992) such as the ERP. Discrete wavelet transforms (DWT; Shensa, 1991) represent signals as temporally ordered coefficients in different scales of a time-frequency plane. More precisely, the DWT represents signals in a time-scale plane, where scale is related to -but not identical with -frequency. The concept of scale comes from the dilation of a "mother wavelet" in the time domain. Each dilation is a doubling of the wavelet length in the time domain which results in a halving of the bandwidth in the