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What is g.BSanalyze?

g.BSanalyze is an interactive platform that facilitates the processing and analysis of multimodal biosignal data, specifically targeted at clinical research and life sciences. With over 20 years in the market, it has established a presence in more than 70 countries around the globe. This software is recognized as the most comprehensive toolkit available for assessing both non-invasive and invasive functions and dysfunctions related to the brain, heart, and muscles. Its outstanding performance has earned it numerous international accolades. The newest version introduces a wide array of features, including topographic plots, Canonical Correlation Analysis (CCA), advanced filtering capabilities, a cortiQ file importer, improvements for Result2D, and specialized tools for analyzing Cortico-Cortical Evoked Potentials, as well as dedicated toolboxes for electrocorticography (ECoG) and transcranial magnetic stimulation (TMS). The software is also equipped with various sample biosignal datasets encompassing P300, SSVEP, motor imagery, CSP BCIs, Tilt-Table, event-related potentials (EPs), multi-unit activity, continuous flow magnetic (CFM), cortico-cortical evoked potentials (CCEP), and ERD/ERS. Additionally, it features a highly interactive and user-friendly graphical interface that supports the analysis and documentation of EEG, ECoG, EOG, EMG, ECG, spike data, and diverse physical datasets within the MATLAB environment. In summary, g.BSanalyze emerges as a robust solution for researchers and practitioners eager to explore the intricate realms of biosignal analysis, making it an invaluable asset in the field. Furthermore, its continuous updates ensure that users have access to the latest advancements in biosignal research.

What is MatConvNet?

The open source library VLFeat provides an extensive selection of renowned algorithms aimed at computer vision, excelling in tasks like image understanding and the matching and extraction of local features. Its diverse set of algorithms includes Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, the agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, and large scale SVM training, among others. Written in C for optimal performance and compatibility, it features MATLAB interfaces that improve user accessibility and is supported by detailed documentation. This library works seamlessly across various operating systems such as Windows, Mac OS X, and Linux, which enhances its usability across multiple platforms. Furthermore, the MatConvNet toolbox is specifically crafted for MATLAB, focusing on the implementation of Convolutional Neural Networks (CNNs) for a range of computer vision tasks. Renowned for its user-friendliness and efficiency, MatConvNet allows for the execution and training of advanced CNNs, offering numerous pre-trained models suited for applications like image classification, segmentation, face detection, and text recognition. The synergistic use of these powerful tools delivers a comprehensive framework that supports researchers and developers in advancing their projects in computer vision, ensuring they are equipped with cutting-edge resources and capabilities. This combination fosters innovation within the field by enabling seamless experimentation and development.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

$0.02/one-time

Pricing Information

Pricing not provided

Supported Platforms

Windows

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Guger Technologies

Company Location

Austria

Company Website

www.gtec.at/product/gbsanalyze/

Company Facts

Organization Name

VLFeat

Company Location

United States

Company Website

www.vlfeat.org/matconvnet/

Categories and Features

Clinical Trial Management

Electronic Data Capture

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation

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