Adding a Custom Biosignal Feature

Below is an example of how to create and register a new feature by inheriting from a base filter class from MyoVerse.

This custom feature calculates the variance of the input signal.

We Recommend Implementing any Additions in user_config.py

The user_config.py module is specifically designed for end-users to register and configure their own custom components such as models, features, and filters. This keeps your modifications modular, reduces conflicts with core MyoGestic settings, and simplifies upgrades in the future.

Important

By registering your addition in user_config.py, you ensure that your custom configuration stays separate from core MyoGestic functionality and remains compatible with future updates.

Example Overview

1. Define a new feature class inheriting from the base class.

2. Implement the core logic in the _filter method.

3. Register the feature so it becomes available globally.

import numpy as np
from myogestic.utils.config import CONFIG_REGISTRY
# Ensure this import references your actual base class location:
from myoverse.datasets.filters._template import FilterBaseClass

Step 1: Inherit and define logic

class MyVarianceFeature(FilterBaseClass):
    """
    A feature that computes the variance of the input signal.

    Parameters
    ----------
    input_is_chunked : bool, optional
        Indicates whether the input data is chunked.
    allowed_input_type : Literal["both", "chunked", "not chunked"], optional
        Whether the filter accepts chunked, unchunked, or both.
    is_output : bool, optional
        If True, this feature's output is set as a final output in the pipeline.
    """

    def _filter(self, input_array: np.ndarray) -> np.ndarray:
        """
        Compute the variance of the input signal.

        Parameters
        ----------
        input_array : np.ndarray
            The data on which variance is computed.

        Returns
        -------
        np.ndarray
            The variance output as a single-element array, for consistency.
        """
        return np.array([np.var(input_array)], dtype=float)

Step 2: Register the custom feature

CONFIG_REGISTRY.register_feature("My Variance Feature", MyVarianceFeature)

Example Usage

if __name__ == "__main__":
    # Create a small signal for demonstration
    sample_data = np.array([1.2, 2.5, 2.7, 2.8, 3.1])

    # Instantiate and apply the feature
    feature_instance = MyVarianceFeature(
        input_is_chunked=False,
        allowed_input_type="not chunked"
    )
    variance_value = feature_instance(sample_data)

    print(f"Variance of {sample_data} = {variance_value[0]:.4f}")

Variance of [1.2 2.5 2.7 2.8 3.1] = 0.4344

Estimated memory usage: 528 MB