![]() There are two main problems in these types of situations: overlapping neural responses from subsequent events and complex influences of nuisance variables that cannot be fully controlled. ![]() Examples include laboratory studies with fast and concurrent streams of visual, auditory, and tactile stimuli ( Spitzer, Blankenburg & Summerfield, 2016), experiments that combine EEG recordings with eye-tracking recordings during natural vision ( Dimigen et al., 2011), EEG studies in virtual reality ( Ehinger et al., 2014) or mobile brain/body imaging studies that investigate real-world interactions of freely moving participants ( Gramann et al., 2014). In recent years, however, there has been a rising interest in recording brain-electric activity also in more complex paradigms and naturalistic situations. In many cases, each experimental trial involves only a single, tightly controlled stimulation, and a single manual response. Įvent-related brain responses in the electroencephalogram (EEG) are traditionally studied in strongly simplified and strictly orthogonal stimulus-response paradigms. It is available as open-source software at. 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. We illustrate the advantages of this approach for simulated data as well as data from a standard face recognition experiment. It also includes advanced options for regularization and the use of temporal basis functions (e.g., Fourier sets). The toolbox is modular, compatible with EEGLAB and can handle even large datasets efficiently. 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. 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. 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. In addition, the recorded neural activity is typically modulated by numerous covariates, which influence the measured responses in a linear or non-linear fashion. As a result, electrophysiological responses from subsequent events often overlap with each other. ![]() This extension was inspired from, but the code reuses the core-JupyterLab filebrowser and replaces it by default.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. Then you can remove the symlink named jupyterlab-unfold within that folder. To find its location, you can run jupyter labextension list to figure out where the labextensionsįolder is located. In development mode, you will also need to remove the symlink created by jupyter labextension developĬommand. To also generate source maps for the JupyterLab core extensions, you can run the following command: jupyter lab build -minimize =Falseĭevelopment uninstall pip uninstall jupyterlab-unfold Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).īy default, the jlpm run build command generates the source maps for this extension to make it easier to debug using the browser dev tools. With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. # Watch the source directory in one terminal, automatically rebuilding when needed You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension. # Rebuild extension Typescript source after making changes # Link your development version of the extension with JupyterLab # Clone the repo to your local environment # Change directory to the jupyterlab-unfold directory # Install package in development mode The jlpm command is JupyterLab's pinned version of Note: You will need NodeJS to build the extension package. To remove the extension, execute: pip uninstall jupyterlab-unfold To install the extension, execute: pip install jupyterlab-unfold
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