Change Detection and Gaussian Process Inference in Piecewise Stationary Environments Under Noisy Inputs

Imbiriba, Tales; LaMountain, Gerald; Wu, Peng; Erdoğmuş, Deni̇z; Closas, Pau

doi:10.1109/camsap45676.2019.9022469

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…(3) with respect to (σ 2 η , θ). GP regression has been successfully used in a wide variety of fields including regression and classification [27], detection [28], [29], unmixing [30], and Bayesian optimization [31]. One limitation of GP is related to the Gaussian assumption on the posterior distribution, which makes standard GP models unfit or inaccurate when the observation distribution is non-Gaussian.…”

Section: Gaussian Process For Tms Mappingmentioning

confidence: 99%

Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning

Faghihpirayesh

Yarossi

Imbiriba

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Transcranial Magnetic Stimulation (TMS) can be used to map cortical motor topography by spatially sampling the sensorimotor cortex while recording Motor Evoked Potentials (MEP) with surface electromyography (EMG). Traditional sampling strategies are time-consuming and inefficient, as they ignore the fact that responsive sites are typically sparse and highly spatially correlated. An alternative approach, commonly employed when TMS mapping is used for presurgical planning, is to leverage the expertise of the coil operator to use MEPs elicited by previous stimuli as feedback to decide which loci to stimulate next. In this paper, we propose to automatically infer optimal future stimulus loci using active learning Gaussian Process-based sampling in place of user expertise. We first compare the user-guided (USRG) method to the traditional grid selection method and randomized sampling to verify that the USRG approach has superior performance. We then compare several novel active Gaussian Process (GP) strategies with the USRG approach. Experimental results using real data show that, as expected, the USRG method is superior to the grid and random approach in both time efficiency and MEP map accuracy. We also found that an active warped GP entropy and a GP random-based strategy performed equally as well as, or even better than, the USRG method. These methods were completely automatic, and succeeded in efficiently sampling the regions in which the MEP response variations are largely confined. This work provides the foundation for highly efficient, fully automatized TMS mapping, especially when considered in the context of advances in robotic coil operation.

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Section: Gaussian Process For Tms Mappingmentioning

confidence: 99%

Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning

Faghihpirayesh

Yarossi

Imbiriba

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Saatçi et al (2010) propose the extension of BOCPD to a GP framework. Developments related to GPs for change monitoring include simultaneous inference and change detection (Imbiriba et al, 2019), sparse GPs for faster computation (Gu et al, 2020), multi-scale GP regression (Susiluoto et al, 2020), and covariance monitoring (Kuhn et al, 2014;Horvath et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data

Wendelberger¹,

Gray²,

Houborg³

et al. 2022

Journal of Data Science

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Global earth monitoring aims to identify and characterize land cover change like construction as it occurs. Remote sensing makes it possible to collect large amounts of data in near real-time over vast geographic areas and is becoming available in increasingly fine temporal and spatial resolution. Many methods have been developed for data from a single pixel, but monitoring pixel-wise spectral measurements over time neglects spatial relationships, which become more important as change manifests in a greater number of pixels in higher resolution imagery compared to moderate resolution. Building on our previous robust online Bayesian monitoring (roboBayes) algorithm, we propose monitoring multiresolution signals based on a wavelet decomposition to capture spatial change coherence on several scales to detect change sites. Monitoring only a subset of relevant signals reduces the computational burden. The decomposition relies on gapless data; we use 3 m Planet Fusion Monitoring data. Simulations demonstrate the superiority of the spatial signals in multiresolution roboBayes (MR roboBayes) for detecting subtle changes compared to pixel-wise roboBayes. We use MR roboBayes to detect construction changes in two regions with distinct land cover and seasonal characteristics: Jacksonville, FL (USA) and Dubai (UAE). It achieves site detection with less than two thirds of the monitoring processes required for pixel-wise roboBayes at the same resolution.

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Enhancing Particle Filtering using Gaussian Processes

Imbiriba

Closas

2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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Change Detection and Gaussian Process Inference in Piecewise Stationary Environments Under Noisy Inputs

Cited by 3 publications

References 11 publications

Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning

Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning

Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data

Enhancing Particle Filtering using Gaussian Processes

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