Leonard J. Trejo

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A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the latent variables (components). However, in contrast to PCR, PLS(More)
We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target(More)
A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a support vector classifier. Unlike principal component analysis (PCA), which has previously served as a dimension reduction step for discrimination problems, orthonormalized PLS is(More)
This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer(More)
In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey-Glass time-series prediction in a noisy environment and(More)
We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies(More)
39 40 41 In this paper, we propose the application of the 42 Kernel Principal Component Analysis (PCA) tech43 nique for feature selection in a high-dimensional 44 feature space, where input variables are mapped by 45 a Gaussian kernel. The extracted features are 46 employed in the regression problems of chaotic 47 Mackey–Glass time-series prediction in a(More)
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 DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models(More)
In kernel based methods such as Support Vector Machines, Kernel PCA, Gaussian Processes or Regularization Networks the computational requirements scale as O(n3) where n is the number of training points. In this paper we investigate Kernel Principal Component Regression (KPCR) with the Expectation Maximization approach in estimating of the subset of p(More)
Two new computational models show that the EEG distinguishes three distinct mental states ranging from alert to fatigue. State 1 indicates heightened alertness and is frequently present during the first few minutes of time on task. State 2 indicates normal alertness, often following and lasting longer than State 1. State 3 indicates fatigue, usually(More)