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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Optimized principal component analysis on coronagraphic images of the fomalhaut system

We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases the background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M {sub Jup} from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Prostate cancer invasion and metastasis: Insights from mining genomic data

Prostate cancer (PCa) is the second most commonly diagnosed malignancy in men in the Western world and the second leading cause of cancer-related deaths among men worldwide. Although most cancers have the potential to metastasize under appropriate conditions, PCa favors the skeleton as a primary site of metastasis, suggesting that the bone microenvironment is conducive to its growth. PCa metastasis proceeds through a complex series of molecular events that include angiogenesis at the site of the original tumor, local migration within the primary site, intravasation into the blood stream, survival within the circulation, extravasation of the tumor cells to the target organ and colonization of those cells within the new site. In turn, each one of these steps involves a complicated chain of events that utilize multiple protein–protein interactions, protein signaling cascades and transcriptional changes. Despite the urgent need to improve current biomarkers for diagnosis, prognosis and drug resistance, advances have been slow. Global gene expression methods such as gene microarrays and RNA sequencing enable the study of thousands of genes simultaneously and allow scientists to examine molecular pathways of cancer pathogenesis. In this review, we summarize the current literature that explored high-throughput transcriptome analysis toward the advancement of biomarker discovery for PCa. Novel biomarkers are strongly needed to enable more accurate detection of PCa, improve prediction of tumor aggressiveness and facilitate the discovery of new therapeutic targets for tailored medicine. Furthermore, promising molecular markers identified from gene expression profiling studies include HPN, CLU1, WT1, WNT5A, AURKA and SPARC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A reversible Renilla luciferase protein complementation assay for rapid identification of protein-protein interactions reveals the existence of an interaction network involved in xyloglucan biosynthesis in the plant Golgi apparatus

A growing body of evidence suggests that protein–protein interactions (PPIs) occur amongst glycosyltransferases (GTs) required for plant glycan biosynthesis (e.g. cell wall polysaccharides and N-glycans) in the Golgi apparatus, and may control the functions of these enzymes. However, identification of PPIs in the endomembrane system in a relatively fast and simple fashion is technically challenging, hampering the progress in understanding the functional coordination of the enzymes in Golgi glycan biosynthesis. To solve the challenges, we adapted and streamlined a reversible Renilla luciferase protein complementation assay (Rluc-PCA), originally reported for use in human cells, for transient expression in Nicotiana benthamiana. We tested Rluc-PCA and successfully identified luminescence complementation amongst Golgi-localizing GTs known to form a heterodimer (GAUT1 and GAUT7) and those which homooligomerize (ARAD1). In contrast, no interaction was shown between negative controls (e.g. GAUT7, ARAD1, IRX9). Rluc-PCA was used to investigate PPIs amongst Golgi-localizing GTs involved in biosynthesis of hemicelluloses. Although no PPI was identified among six GTs involved in xylan biosynthesis, Rluc-PCA confirmed three previously proposed interactions and identified seven novel PPIs amongst GTs involved in xyloglucan biosynthesis. Notably, three of the novel PPIs were confirmed by a yeast-based split-ubiquitin assay. Finally, Gateway-enabled expression vectors were generated, allowing rapid construction of fusion proteins to the Rluc reporters and epitope tags. In conclusion, our results show that Rluc-PCA coupled with transient expression in N. benthamiana is a fast and versatile method suitable for analysis of PPIs between Golgi resident proteins in an easy and mid-throughput fashion in planta.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Non-Gaussianity in the weak lensing correlation function likelihood – implications for cosmological parameter biases

ABSTRACT We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in 1D marginal distributions of shear two-point correlation functions in simulated weak lensing data. We examine the implications in the context of future surveys, in particular LSST, with derivations of how the non-Gaussianity scales with survey area. We show that there is no significant bias in 1D posteriors of Ωm and σ8 due to the non-Gaussian likelihood distributions of shear correlations functions using the mock data (100 deg2). We also present a systematic approach to constructing approximate multivariate likelihoods with 1D parametric functions by assuming independence or more flexible non-parametric multivariate methods after decorrelating the data points using principal component analysis (PCA). While the use of PCA does not modify the non-Gaussianity of the multivariate likelihood, we find empirically that the 1D marginal sampling distributions of the PCA components exhibit less skewness and kurtosis than the original shear correlation functions. Modelling the likelihood with marginal parametric functions based on the assumption of independence between PCA components thus gives a lower limit for the biases. We further demonstrate that the difference in cosmological parameter constraints between the multivariate Gaussian likelihood model and more complex non-Gaussian likelihood models would be even smaller for an LSST-like survey. In addition, the PCA approach automatically serves as a data compression method, enabling the retention of the majority of the cosmological information while reducing the dimensionality of the data vector by a factor of ∼5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-E-J-151: Day-To-Day Variations in Fraction-Specific Motion Modeling Using Patient 4DCBCT Images

Purpose: The goal of this study is to quantify the interfraction reproducibility of patient-specific motion models derived from 4DCBCT acquired on the day of treatment of lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived from patient 4DCBCT images acquired daily over 3–5 fractions of treatment by 1) applying deformable image registration between each 4DCBCT image and a reference phase from that day, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs to derive a motion model. The motion model from the first day of treatment is compared to motion models from each successive day of treatment to quantify variability in motion models generated from different days. Four SBRT patient datasets have been acquired thus far in this IRB approved study. Results: Fraction-specific motion models for each fraction and patient were derived and PCA eigenvectors and their associated eigenvalues are compared for each fraction. For the first patient dataset, the average root mean square error between the first two eigenvectors associated with the highest two eigenvalues, in four fractions was 0.1, while it was 0.25 between the last three PCA eigenvectors associated with the lowest three eigenvalues. It was found that the eigenvectors and eigenvalues of PCA motion models for each treatment fraction have variations and the first few eigenvectors are shown to be more stable across treatment fractions than others. Conclusion: Analysis of this dataset showed that the first two eigenvectors of the PCA patient-specific motion models derived from 4DCBCT were stable over the course of several treatment fractions. The third, fourth, and fifth eigenvectors had larger variations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-426: Feasibility of Stereotactic Body Radiation Therapy (SBRT) Treatment of Pancreatic Cancer Using Volumetric Modulated Arc Therapy (VMAT) with Active Breathing Control (ABC)

Purpose: SBRT shows excellent tumor control and toxicity rates for patients with locally advanced pancreatic cancer (PCA). Herein, we evaluate the feasibility of using VMAT with ABC for PCA SBRT. Methods: Nine PCA patients previously treated via SBRT utilizing 11-beam step-and-shoot IMRT technique in our center were retrospectively identified, among whom eight patients received 3300cGy in 5 fractions while one received 3000cGy in 5 fractions. A VMAT plan was generated on each patient’s planning CT in Pinnacle v9.8 on Elekta Synergy following the same PCA SBRT clinical protocol. Three partial arcs (182°–300°, 300°-60°, and 60°-180°) with 2°/4° control-point spacing were used. The dosimetric difference between the VMAT and the original IMRT plans was analyzed. IMRT QA was performed for the VMAT plans using MapCheck2 in MapPHAN and the total delivery time was recorded. To mimic the treatment situation with ABC, where patients hold their breath for 20–30 seconds, the delivery was intentionally interrupted every 20–30 seconds. For each plan, the QA was performed with and without beam interruption. Gamma analysis (2%/2mm) was used to compare the planned and measured doses. Results: All VMAT plans with 2mm dose grid passed the clinic protocol with similar PTV coverage and OARs sparing, where PTV V-RxDose was 92.7±2.1% (VMAT) vs. 92.1±2.6% (IMRT), and proximal stomach V15Gy was 3.60±2.69 cc (VMAT) vs. 4.80±3.13 cc (IMRT). The mean total MU and delivery time of the VMAT plans were 2453.8±531.1 MU and 282.1±56.0 seconds. The gamma passing rates of absolute dose were 94.9±3.4% and 94.5±4.0% for delivery without and with interruption respectively, suggesting the dosimetry of VMAT delivery with ABC for SBRT won’t be compromised. Conclusion: This study suggests that PCA SBRT using VMAT with ABC is a feasible technique without compromising plan dosimetry. The combination of VMAT with ABC will potentially reduce the SBRT treatment time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit more descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction reliability in such low dimensional manifold. The fixed-point iterative approach turns out to work well practically for the pre-image recovery. Our approach is particularly suitable to facilitate managing respiratory motion in image-guide radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Targeting prostate tumor low–molecular weight tyrosine phosphatase for oxidation-sensitizing therapy

Protein tyrosine phosphatases (PTPs) play major roles in cancer and are emerging as therapeutic targets. Recent reports suggest low–molecular weight PTP (LMPTP)—encoded by the ACP1 gene—is overexpressed in prostate tumors. We found ACP1 up-regulated in human prostate tumors and ACP1 expression inversely correlated with overall survival. Using CRISPR-Cas9–generated LMPTP knockout C4-2B and MyC-CaP cells, we identified LMPTP as a critical promoter of prostate cancer (PCa) growth and bone metastasis. Through metabolomics, we found that LMPTP promotes PCa cell glutathione synthesis by dephosphorylating glutathione synthetase on inhibitory Tyr 270 . PCa cells lacking LMPTP showed reduced glutathione, enhanced activation of eukaryotic initiation factor 2–mediated stress response, and enhanced reactive oxygen species after exposure to taxane drugs. LMPTP inhibition slowed primary and bone metastatic prostate tumor growth in mice. These findings reveal a role for LMPTP as a critical promoter of PCa growth and metastasis and validate LMPTP inhibition as a therapeutic strategy for treating PCa through sensitization to oxidative stress.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

The Value of {sup 99m}Tc-PSMA SPECT/CT-Guided Surgery for Identifying and Locating Lymph Node Metastasis in Prostate Cancer Patients

Background: This study evaluated the effect of technetium-99m ({sup 99m}Tc)-labeled prostate-specific membrane antigen (PSMA)-based image-guided surgery on the oncologic outcomes for patients with primary or recurrent prostate cancer (PCa). Methods: This study retrospectively analyzed 54 consecutive patients with PCa who underwent {sup 99m}Tc-labeled PSMA-based image-guided surgery between January 2016 and September 2017. These patients received a radical prostatectomy (RP) with pelvic lymph node dissection (PLND) or salvage lymph node dissection (sLND). The resected specimens were compared with findings of postoperative histologic analysis. The responses to the treatment were recorded during the follow-up period. Results: In 31 patients, PSMA single-photon emission computerized tomography (SPECT) and computed tomography (CT) could find 52 suspicious lymph node metastases (LNMs). With the help of PSMA SPECT/CT, 12 patients with recurrence received sLND, 19 primary PCa patients received RP with extended PLND, and 23 primary PCa patients received RP with standard PLND. The findings showed that PSMA SPECT/CT could detect LNMs with high sensitivity and specificity. In six patients, PSMA SPECT/CT could find more LNMs that were not found by MRI and help to modify the extent of lymphadenectomy. At the latest follow-up evaluation, 39 patients showed a biochemical response (BR), 9 patients showed a biochemical recurrence (BCR) after BR, and 6 patients never exhibited BR. The patients who received RP with standard PLND or extended PLND had a better prostate-specific antigen (PSA) response than the patients who received sLND. The patients with pelvic LNMs also had a better PSA response than the patients with retroperitoneal LNMs. Conclusions: This study showed that {sup 99m}Tc-PSMA SPECT/CT-guided surgery can remove more LNMs than conventional imaging with high sensitivity and specificity and delay disease progression in PCa patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Highly efficient terahertz photoconductive metasurface detectors operating at microwatt-level gate powers

Despite their wide use in terahertz (THz) research and technology, the application spectra of photoconductive antenna (PCA) THz detectors are severely limited due to the relatively high optical gating power requirement. This originates from poor conversion efficiency of optical gate beam photons to photocurrent in materials with sub-picosecond carrier lifetimes. Here we show that using an ultra-thin (160 nm), perfectly absorbing low-temperature grown GaAs metasurface as the photoconductive channel drastically improves the efficiency of THz PCA detectors. This is achieved through perfect absorption of the gate beam in a significantly reduced photoconductive volume, enabled by the metasurface. This Letter demonstrates that sensitive THz PCA detection is possible using optical gate powers as low as 5 µW—three orders of magnitude lower than gating powers used for conventional PCA detectors. We show that significantly higher optical gate powers are not necessary for optimal operation, as they do not improve the sensitivity to the THz field. This class of efficient PCA THz detectors opens doors for THz applications with low gate power requirements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Application of Principal Component Analysis to Electrochemical Reprocessing PM and NMAC

In this report, data from an electrorefiner (ER) for nuclear fuel reprocessing is evaluated for process monitoring (PM) conclusions. This data comes from tests performed at the Idaho National Laboratory in 2022. Multivariate approaches utilizing methods of Principal Component Analysis (PCA) is applied. This is based off established work in process monitoring for fault detection in industrial facilities. This report will discuss the background, methods, and results of the application and some of the conclusions and applications that can be drawn from them. PCA is applied to two different electrorefiner (ER) operations that occurred at Idaho National Laboratory between August and October 2022. The first operation occurred with little incident while the second had several noted faults in the equipment in operational logs. The data from the first run was used to train the data for “normal” operations and applied to both sets of data to determine when operations were in an “off-normal” condition and identify where the fault occurs through PCA. PCA was able to identify off-normal events and identify the cause for off-normal operations. These identified off-normal events matched with the events and their causes in the operational logs. However, small amounts of variance in the data led to false detection of “off-normal” events. Thus, careful selection of training data and a-posteriori conclusions based off operator assessments will both be required for application of PCA to PM applications. This work demonstrated that multivariate approaches and latent variables are applicable to pyroprocessing PM applications and can be further expanded in future work as quality variables such as salt concentration from sensors and sampling become available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1994

Flight testing a propulsion-controlled aircraft emergency flight control system on an F-15 airplane

Flight tests of a propulsion-controlled aircraft (PCA) system on an F-15 airplane have been conducted at the NASA Dryden Flight Research Center. The airplane was flown with all flight control surfaces locked both in the manual throttles-only mode and in an augmented system mode. In the latter mode, pilot thumbwheel commands and aircraft feedback parameters were used to position the throttles. Flight evaluation results showed that the PCA system can be used to land an airplane that has suffered a major flight control system failure safely. The PCA system was used to recover the F-15 airplane from a severe upset condition, descend, and land. Pilots from NASA, U.S. Air Force, U.S. Navy, and McDonnell Douglas Aerospace evaluated the PCA system and were favorably impressed with its capability. Manual throttles-only approaches were unsuccessful. This paper describes the PCA system operation and testing. It also presents flight test results and pilot comments.

Burcham, F. W., Jr.↗
NASA NTRS1997

Piloted Simulation Tests of Propulsion Control as Backup to Loss of Primary Flight Controls for a B747-400 Jet Transport

Partial failures of aircraft primary flight control systems and structural damages to aircraft during flight have led to catastrophic accidents with subsequent loss of lives (e.g. DC-10, B-747, C-5, B-52, and others). Following the DC-10 accident at Sioux City, Iowa in 1989, the National Transportation Safety Board recommended 'Encourage research and development of backup flight control systems for newly certified wide-body airplanes that utilize an alternate source of motive power separate from that source used for the conventional control system.' This report describes the concept of a propulsion controlled aircraft (PCA), discusses pilot controls, displays, and procedures; and presents the results of a PCA piloted simulation test and evaluation of the B747-400 airplane conducted at NASA Ames Research Center in December, 1996. The purpose of the test was to develop and evaluate propulsion control throughout the full flight envelope of the B747-400 including worst case scenarios of engine failures and out of trim moments. Pilot ratings of PCA performance ranged from adequate to satisfactory. PCA performed well in unusual attitude recoveries at 35,000 ft altitude, performed well in fully coupled ILS approaches, performed well in single engine failures, and performed well at aft cg. PCA performance was primarily limited by out-of-trim moments.

Bull, John↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

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 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 f ree 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 DWT network 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. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS2002

Indirect Field Measurement of Wine-Grape Vineyard Canopy Leaf Area Index

Leaf area index (LAI) indirect measurements were made at 12 study plots in California's Napa Valley commercial wine-grape vineyards with a LI-COR LI-2000 Plant Canopy Analyzer (PCA). The plots encompassed different trellis systems, biological varieties, and planting densities. LAI ranged from 0.5 - 2.25 sq m leaf area/ sq m ground area according to direct (defoliation) measurements. Indirect LAI reported by the PCA was significantly related to direct LAI (r(exp 2) = 0.78, p less than 001). However, the PCA tended to underestimate direct LAI by about a factor of two. Narrowing the instrument's conical field of view from 148 deg to 56 deg served to increase readings by approximately 30%. The PCA offers a convenient way to discern relative differences in vineyard canopy density. Calibration by direct measurement (defoliation) is recommended in cases where absolute LAI is desired. Calibration equations provided herein may be inverted to retrieve actual vineyard LAI from PCA readings.

Johnson, Lee F.↗
NASA NTRS1998

High Performance Parallel Architectures

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operational. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, aimed at concentrating the vital information and discarding redundant data. One such transformation, which is widely used in remote sensing, is the Principal Components Analysis (PCA). This report summarizes our progress on the development of a parallel PCA and its implementation on two Beowulf cluster configuration; one with fast Ethernet switch and the other with a Myrinet interconnection. Details of the implementation and performance results, for typical sets of multispectral and hyperspectral NASA remote sensing data, are presented and analyzed based on the algorithm requirements and the underlying machine configuration. It will be shown that the PCA application is quite challenging and hard to scale on Ethernet-based clusters. However, the measurements also show that a high- performance interconnection network, such as Myrinet, better matches the high communication demand of PCA and can lead to a more efficient PCA execution.

El-Ghazawi, Tarek↗
NASA NTRS2003

Ising model of cardiac thin filament activation with nearest-neighbor cooperative interactions

We have developed a model of cardiac thin filament activation using an Ising model approach from equilibrium statistical physics. This model explicitly represents nearest-neighbor interactions between 26 troponin/tropomyosin units along a one-dimensional array that represents the cardiac thin filament. With transition rates chosen to match experimental data, the results show that the resulting force-pCa (F-pCa) relations are similar to Hill functions with asymmetries, as seen in experimental data. Specifically, Hill plots showing (log(F/(1-F)) vs. log [Ca]) reveal a steeper slope below the half activation point (Ca(50)) compared with above. Parameter variation studies show interplay of parameters that affect the apparent cooperativity and asymmetry in the F-pCa relations. The model also predicts that Ca binding is uncooperative for low [Ca], becomes steeper near Ca(50), and becomes uncooperative again at higher [Ca]. The steepness near Ca(50) mirrors the steep F-pCa as a result of thermodynamic considerations. The model also predicts that the correlation between troponin/tropomyosin units along the one-dimensional array quickly decays at high and low [Ca], but near Ca(50), high correlation occurs across the whole array. This work provides a simple model that can account for the steepness and shape of F-pCa relations that other models fail to reproduce.

Evaluation Studies↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

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 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 DWT network 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. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗