{"id":155,"date":"2016-09-16T14:01:11","date_gmt":"2016-09-16T18:01:11","guid":{"rendered":"https:\/\/engineering.jhu.edu\/nsa\/?page_id=155"},"modified":"2022-07-21T10:11:18","modified_gmt":"2022-07-21T14:11:18","slug":"links","status":"publish","type":"page","link":"https:\/\/engineering.jhu.edu\/nsa\/links\/","title":{"rendered":"Software"},"content":{"rendered":"<h1>EPViz (EEG Prediction Visualizer)<\/h1>\n<p><a href=\"https:\/\/engineering.jhu.edu\/nsa\/wp-content\/uploads\/2020\/08\/Visualizer.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-2789 \" src=\"https:\/\/engineering.jhu.edu\/nsa\/wp-content\/uploads\/2020\/08\/Visualizer-300x171.png\" alt=\"\" width=\"338\" height=\"198\" \/><\/a>Scalp EEG is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in the community towards spatio-temporal predictive analyses. We have developed the EEG Prediction Visualizer (EPViz) to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to the EEG, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. There is also a command-line option for batch processing. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic statistics, data anonymization, and annotation editing.<\/p>\n<p>EPViz can be installed in three ways: (1) cloning our GitHub repository to access the latest version, (2) through PyPI, and (3) as a standalone prepackaged application for MacOS and Windows. [<a href=\"https:\/\/github.com\/jcraley\/epviz\">github<\/a>][<a href=\"https:\/\/pypi.org\/project\/EPViz\/\">pypi<\/a>][<a href=\"https:\/\/drive.google.com\/file\/d\/1h4gQzh4R9LUFekecd8N42QnHRV9H9lUf\/view?usp=sharing\">Mac App<\/a>][<a href=\"https:\/\/drive.google.com\/file\/d\/1V8-sWYZ-cVQ3cTAEh8ghCkeTzC2Mgfip\/view?usp=sharing\">Windows App<\/a>].<\/p>\n<p>Please visit our <strong><a href=\"https:\/\/engineering.jhu.edu\/nsa\/epviz\/\">EPViz page<\/a><\/strong> for the complete user guide.<\/p>\n<h1>EDF Anonymizer<\/h1>\n<p><a href=\"https:\/\/engineering.jhu.edu\/nsa\/wp-content\/uploads\/2020\/08\/EDFAnonymizer.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-2792 alignleft\" src=\"https:\/\/engineering.jhu.edu\/nsa\/wp-content\/uploads\/2020\/08\/EDFAnonymizer-150x150.png\" alt=\"\" width=\"183\" height=\"183\" \/><\/a>We have also provided just the anonymization tool in EPViz as a standalone module. This tool allows the user to alter the EDF header fields and provides default settings for scrubbing patient IDs and time stamps. [<a href=\"https:\/\/github.com\/jcraley\/jhu-eeg\/\">github<\/a>][<a href=\"https:\/\/drive.google.com\/file\/d\/1OYpG7epplYmMcuTNUeTTKZR3bY_7m4LD\/view?usp=sharing\">Windows App<\/a>][<a href=\"https:\/\/drive.google.com\/file\/d\/1Gj1k5LtVUXGKjwWUkHZu9D353TWznv6M\/view?usp=sharing\">MAC App<\/a>]<\/p>\n<h1>Epilepsy<\/h1>\n<ul>\n<li>N. Nandakumar, D. Hsu, R. Ahmed, A. Venkataraman. <em>DeepSOZ: A Graph Convolutional Network for Automated Seizure Onset Zone Localization from Resting-State fMRI Connectivity<\/em>. Under Revision for IEEE Transactions on Biomedical Engineering, 2022. [paper][<a href=\"https:\/\/github.com\/NareshNandakumarJHU\/DeepEZ_GCN\">github<\/a>]<\/li>\n<\/ul>\n<h1>Predictive Connectomics<\/h1>\n<ul>\n<li style=\"margin-bottom: 10px;\">N.S. D&#8217;Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. <em>A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes<\/em>. In Proc. MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS (to appear), 2021. [paper][<a href=\"https:\/\/github.com\/Niharika-SD\/Matrix-Autoencoder\">github<\/a>]<\/li>\n<li style=\"margin-bottom: 10px;\">N.S. D&#8217;Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. <em>M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations<\/em>. In Proc. MIDL: Medical Imaging with Deep Learning, MLR:1-12, 2021. [<a href=\"https:\/\/openreview.net\/pdf\/1b4e075b216f9bd08e41e697451bda908f109245.pdf\">paper<\/a>][<a href=\"https:\/\/github.com\/Niharika-SD\/M-GCN\">github<\/a>]<\/li>\n<li style=\"margin-bottom: 10px;\">N.S. D&#8217;Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman. <em>A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism<\/em>. In Proc. MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12267:437-447, 2020. [<a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-59728-3_43\">paper<\/a>][<a href=\"https:\/\/github.com\/Niharika-SD\/Deep-sr-DDL\">github<\/a>]<\/li>\n<li style=\"margin-bottom: 10px;\">N.S. D&#8217;Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman. <em>A Joint Network Optimization Framework to Predict Clinical Severity from Resting State fMRI Data<\/em>. NeuroImage, 206:116314, 2020. [<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S105381191930905X\">paper<\/a>][<a href=\"https:\/\/github.com\/Niharika-SD\/JNO\">github<\/a>]<\/li>\n<\/ul>\n<h1>Imaging-Genetics<\/h1>\n<ul>\n<li>A Generative Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space [<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811921004778\">paper<\/a>][<a href=\"https:\/\/github.com\/sayangsep\/Imaging-genetics\">github<\/a>]<\/li>\n<\/ul>\n<h1>Preoperative Mapping with Resting-State fMRI<\/h1>\n<ul>\n<li>A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize The Eloquent Cortex in Brain Tumor Patients [paper][<a href=\"https:\/\/github.com\/NareshNandakumarJHU\/Multiscale-Spatial-Attention-Applied-To-Dynamic-Connectivity\">github<\/a>]<\/li>\n<li>A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients using Both Static and Dynamic Functional Conn [<a href=\"https:\/\/link.springer.com\/chapter\/10.1007%2F978-3-030-66843-3_4\">paper<\/a>][<a href=\"https:\/\/github.com\/NareshNandakumarJHU\/Multi-task-Learning-of-Functional-Networks-For-Dynamic-Connectivity\">github<\/a>]<\/li>\n<\/ul>\n<h1>Emotional Speech<\/h1>\n<ul>\n<li>Sample, Attend and Morph: A Deep-Bayesian Framework for Adaptive Speech Duration Modification [paper][<a href=\"https:\/\/github.com\/ravi-0841\/pytorch-speech-transformer\">github<\/a>]<\/li>\n<li>Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator [<a href=\"https:\/\/www.isca-speech.org\/archive\/Interspeech_2020\/abstracts\/1325.html\">paper<\/a>][<a href=\"https:\/\/github.com\/ravi-0841\/variational-cycle-gan\">github<\/a>]<\/li>\n<li>Multi-Speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network [<a href=\"https:\/\/www.isca-speech.org\/archive\/Interspeech_2020\/abstracts\/1323.html\">paper<\/a>][<a href=\"https:\/\/github.com\/ravi-0841\/Chained-Encoder-Decoder-Predictor\">github<\/a>]<\/li>\n<li>A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion [paper][<a href=\"https:\/\/github.com\/ravi-0841\/spect-pitch-gan\">github<\/a>][<a href=\"https:\/\/engineering.jhu.edu\/nsa\/wp-content\/uploads\/2022\/07\/demo_VCGAN_CGAN.zip\">sample audio<\/a>]<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>EPViz (EEG Prediction Visualizer) Scalp EEG is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in &hellip; <a href=\"https:\/\/engineering.jhu.edu\/nsa\/links\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1476,"featured_media":0,"parent":0,"menu_order":6,"comment_status":"closed","ping_status":"closed","template":"sidebar-page.php","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"coauthors":[],"class_list":["post-155","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Software - Neural Systems Analysis Laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/nsa\/links\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Software - Neural Systems Analysis Laboratory\" \/>\n<meta property=\"og:description\" content=\"EPViz (EEG Prediction Visualizer) Scalp EEG is one of the most popular noninvasive modalities for studying real-time neural phenomena. 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