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brainSimulator

DOI

Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/

brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.

Use

First of all, install the package via pypi with:

pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.

#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])

Cite

F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065

Safeguards

As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.

License

This code is released under the license GPL-3.0+.

About

Brain simulation using KDE or MVN distribution

brainsimulator.readthedocs.io/

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GPL-3.0 license
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
About
Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
Resources
Readme
GPL-3.0 license
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Watchers
3 watching
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1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
About
Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
1 fork
Report repository
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
About
Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
Resources
Readme
GPL-3.0 license
Activity
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Stars
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3 watching
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1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
About
Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
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Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
About
Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
About
Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
1 fork
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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brainSimulator
DOI
Functional brain image synthesis using the KDE or MVN distribution. Currently in beta. Python code. Find the documentation at http://brainsimulator.readthedocs.io/
brainSimulator is a brain image synthesis procedure for data augmentation and standardization of evaluation of ML neuroimaging pipelines. It intends to generate a new image set that share characteristics given an original one. The system focuses on nuclear imaging modalities such as PET or SPECT brain images. It analyses the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, anyone can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space.
Use
First of all, install the package via pypi with:
pip install brainsimulator

With the new version, the whole interface has been switched to an object. This allows to train the model once and then perform as many sample drawings as required.
#navigate to the folder where simulator.py is locatedimportbrainSimulatorassimsimulator=sim.BrainSimulator(algorithm='PCA', method='mvnormal')
simulator.fit(original_dataset, labels) images, classes=simulator.generateDataset(original_dataset, labels, N=200, classes=[0, 1, 2])
Cite
F.J. Martinez-Murcia et al (2017). "Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modelling: Application to Neurodegenerative Diseases." Frontiers in neuroinformatics (online). DOI: 10.3389/fninf.2017.00065
Safeguards
As in the paper, it is best to use MVN modelling, but it is fundamental to test the number of components (L) used in the modelling, otherwise it will lead to overfitting. The KDE modelling works better `out of the box', but the results may be more disperse.
License
This code is released under the license GPL-3.0+.
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Brain simulation using KDE or MVN distribution
brainsimulator.readthedocs.io/
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