API Documentation

Processing Pipeline

MyoTorch core functionality is based on the processing pipeline. The pipeline is a sequence of filters that are applied to data objects.

While pipelines can be used individually, they are most commonly used to create datasets for supervised learning.

The following modules are available for creating pipelines and datasets:

Data Types

MyoTorch offers a variety of datatypes for supervised learning used in different publications

• Data Types

Filters

MyoTorch offers a variety of models for supervised learning used in different publications from the n-squared lab @ FAU Erlangen-Nürnberg.

• Transforms
  • Base Classes
  • Temporal / Signal Processing
  • Normalization
  • Generic Operations
  • Augmentation
  • Spatial / Grid-Aware

Datasets

MyoTorch offers a standard dataset for supervised learning that was used in different publications from the n-squared lab @ FAU Erlangen-Nürnberg.

• Datasets
  • Storage
  • Base Dataset
  • Paradigms
  • Integration
  • Utilities
  • Presets

Models

MyoTorch offers a variety of models for supervised learning used in different publications from the n-squared lab @ FAU Erlangen-Nürnberg.

Note

Only V16 is supported. The older models are available only for reference and inspiration.

• Models
  • RaulNet Models
  • Components