scholarly journals Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7838 ◽  
Author(s):  
Benedikt V. Ehinger ◽  
Olaf Dimigen
Keyword(s):  
Motor Behavior ◽  
Additive Model ◽  
Simulated Data ◽  
Linear Modeling ◽  
Multisensory Stimulation ◽  
Electrophysiological Responses ◽  
Non Linear ◽  
Complex Motor ◽  
Quasi Experimental ◽  
Stimulation Experiments

Electrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or non-linear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined electroencephalogram (EEG)/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even “traditional,” highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g., from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate modeling (“regression-ERPs”), linear deconvolution modeling, and non-linear modeling with the generalized additive model into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g., Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at http://www.unfoldtoolbox.org.

scholarly journals Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

2018 ◽  
Author(s):  
Benedikt V. Ehinger ◽  
Olaf Dimigen
Keyword(s):  
Motor Behavior ◽  
Additive Model ◽  
Simulated Data ◽  
Linear Modeling ◽  
Multisensory Stimulation ◽  
Electrophysiological Responses ◽  
Non Linear ◽  
Complex Motor ◽  
Quasi Experimental ◽  
Stimulation Experiments

ABSTRACTElectrophysiological research with event-related brain potentials (ERPs) is increasingly moving from simple, strictly orthogonal stimulation paradigms towards more complex, quasi-experimental designs and naturalistic situations that involve fast, multisensory stimulation and complex motor behavior. As a result, electrophysiological responses from subsequent events often overlap with each other. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or nonlinear fashion. Examples of paradigms where systematic temporal overlap variations and low-level confounds between conditions cannot be avoided include combined EEG/eye-tracking experiments during natural vision, fast multisensory stimulation experiments, and mobile brain/body imaging studies. However, even “traditional”, highly controlled ERP datasets often contain a hidden mix of overlapping activity (e.g. from stimulus onsets, involuntary microsaccades, or button presses) and it is helpful or even necessary to disentangle these components for a correct interpretation of the results. In this paper, we introduce unfold, a powerful, yet easy-to-use MATLAB toolbox for regression-based EEG analyses that combines existing concepts of massive univariate modeling (“regression ERPs”), linear deconvolution modeling, and non-linear modeling with the generalized additive model (GAM) into one coherent and flexible analysis framework. The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. It also includes advanced options for regularization and the use of temporal basis functions (e.g. Fourier sets). We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. In addition to traditional and non-conventional EEG/ERP designs, unfold can also be applied to other overlapping physiological signals, such as pupillary or electrodermal responses. It is available as open-source software at http://www.unfoldtoolbox.org.

Non-Linear Modeling of Bushings and Cab Mounts for Calculation of Durability Loads

2014 ◽  
Author(s):  
Christian Scheiblegger ◽  
Nantu Roy ◽  
Orlando Silva Parez ◽  
Andrew Hillis ◽  
Peter Pfeffer ◽  
...  
Keyword(s):  
Linear Modeling ◽  
Non Linear

scholarly journals Complex motor task associated with non-linear BOLD responses in cerebro-cortical areas and cerebellum

2015 ◽  
Vol 221 (5) ◽  
pp. 2443-2458 ◽  
Author(s):  
Adnan A. S. Alahmadi ◽  
Rebecca S. Samson ◽  
David Gasston ◽  
Matteo Pardini ◽  
Karl J. Friston ◽  
...  
Keyword(s):  
Motor Task ◽  
Cortical Areas ◽  
Bold Responses ◽  
Non Linear ◽  
Complex Motor

Assessment of nominal data requirements for robust estimation of fractional snow cover in alpine-forested terrain

2021 ◽  
Author(s):  
Elzbieta Wisniewski ◽  
Wit Wisniewski
Keyword(s):  
Snow Cover ◽  
Input Data ◽  
Model Performance ◽  
Slope Aspect ◽  
Ann Model ◽  
Linear Modeling ◽  
Nominal Data ◽  
Fractional Snow Cover ◽  
Non Linear ◽  
Input Variables

<p>The presented research examines what minimum combination of input variables are required to obtain state-of-the-art fractional snow cover (FSC) estimates for heterogeneous alpine-forested terrains. Currently, one of the most accurate FSC estimators for alpine regions is based on training an Artificial Neural Network (ANN) that can deconvolve the relationships among numerous compounded and possibly non-linear bio-geophysical relations encountered in alpine terrain. Under the assumption that the ANN optimally extracts available information from its input data, we can exploit the ANN as a tool to assess the contributions toward FSC estimation of each of the data sources, and combinations thereof. By assessing the quality of the modeled FSC estimates versus ground equivalent data, suitable combinations of input variables can be identified. High spatial resolution IKONOS images are used to estimate snow cover for ANN training and validation, and also for error assessment of the ANN FSC results. Input variables are initially chosen representing information already incorporated into leading snow cover estimators (ex. two multispectral bands for NDSI, etc.). Additional variables such as topographic slope, aspect, and shadow distribution are evaluated to observe the ANN as it accounts for illumination incidence and directional reflectance of surfaces affecting the viewed radiance in complex terrain. Snow usually covers vegetation and underlying geology partially, therefore the ANN also has to resolve spectral mixtures of unobscured surfaces surrounded by snow. Multispectral imagery if therefore acquired in the fall prior to the first snow of the season and are included in the ANN analyses for assessing the baseline reflectance values of the environment that later become modified by the snow. In this study, nine representative scenarios of input data are selected to analyze the FSC performance. Numerous selections of input data combinations produced good results attesting to the powerful ability of ANNs to extract information and utilize redundancy. The best ANN FSC model performance was achieved when all 15 pre-selected inputs were used. The need for non-linear modeling to estimate FSC was verified by forcing the ANN to behave linearly. The linear ANN model exhibited profoundly decreased FSC performance, indicating that non-linear processing more optimally estimates FSC in alpine-forested environments.</p>

scholarly journals Non-linear Relationships Between the Built Environment and Walking Frequency Among Older Adults in Zhongshan, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiani Wu ◽  
Chunli Zhao ◽  
Chaoyang Li ◽  
Tao Wang ◽  
Lanjing Wang ◽  
...  
Keyword(s):  
Older Adults ◽  
Land Use ◽  
Built Environment ◽  
Green Space ◽  
Additive Model ◽  
Walking Behavior ◽  
Old Adults ◽  
Linear Relationships ◽  
Walking Activity ◽  
Non Linear

Aim: Promoting walking activity is an effective way to improve the health of older adults. Walking frequency is a critical component of walking behavior and an essential determinant of daily walking levels. To decipher the association between the built environment and walking frequency among older adults, this study's aims are as follows: (1) to empirically test whether non-linear relationships between the two exist, and (2) to identify the thresholds of the built environment characteristics that promote walking.Methods: The walking frequency of old adults was derived from the Zhongshan Household Travel Survey (ZHTS) in 2012. The sample size of old adults aged 60 or over was 4784 from 274 urban and rural neighborhoods. A semi-parametric generalized additive model (GAMM) is used to analyze the non-linear or non-monotonic relationships between the built environment and the walking frequency among older adults.Results: We found that non-linear relationships exist among five out of the six built environment characteristics. Within certain thresholds, the population density, sidewalk density, bus stop density, land use mixture, and the percentage of green space are positively related to older adults' walking trips. Furthermore, the land use mixture and the percentage of green space show an inverse “V”-shaped relationship.Conclusions: Built environment features can either support or hinder the walking frequency among older adults. The findings in the current study contribute to effective land use and transport policies for promoting active travel among older adults.

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