.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/08_simulate_intramuscular_emg.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_08_simulate_intramuscular_emg.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_08_simulate_intramuscular_emg.py:


Intramuscular EMG Signals
========================

This example demonstrates how to simulate **intramuscular EMG signals** using
needle electrodes. It shows the complete pipeline from muscle model creation
to EMG signal generation with realistic noise and motor unit detectability.

.. note::
    **Intramuscular EMG** (iEMG) is recorded using needle electrodes inserted
    directly into the muscle tissue. This provides high spatial resolution
    and allows for the detection of individual motor unit action potentials
    (MUAPs). Unlike surface EMG, intramuscular recordings can capture the
    activity of deeper motor units and provide better selectivity.

Key Features:
    - **High spatial resolution**: Needle electrodes can detect individual MUAPs
    - **Deep muscle access**: Can record from muscles not accessible by surface electrodes
    - **Motor unit discrimination**: Individual motor units can be identified and tracked
    - **Realistic noise modeling**: Includes physiological noise and recording artifacts

.. GENERATED FROM PYTHON SOURCE LINES 22-23

.. code-block:: Python
   :dedent: 1









.. GENERATED FROM PYTHON SOURCE LINES 25-27

Import Libraries
----------------

.. GENERATED FROM PYTHON SOURCE LINES 27-38

.. code-block:: Python


    import joblib
    import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np
    import seaborn as sns

    from myogen import simulator
    from myogen.utils.currents import create_sinusoidal_current
    from myogen.utils.plotting.spikes import plot_spike_trains








.. GENERATED FROM PYTHON SOURCE LINES 39-42

Define Parameters
-----------------


.. GENERATED FROM PYTHON SOURCE LINES 42-51

.. code-block:: Python


    # Simulation parameters
    N_motor_units = 25  # Smaller number for faster computation
    recruitment_range = 50.0

    # Electrode parameters
    inter_electrode_distance = 0.5  # mm
    electrode_position = (0.0, 0.0, 0.0)  # mm (start of muscle)








.. GENERATED FROM PYTHON SOURCE LINES 52-56

Generate Motor Unit Recruitment Thresholds
-------------------------------------------

First, we generate the **recruitment thresholds** for the motor unit pool.

.. GENERATED FROM PYTHON SOURCE LINES 56-69

.. code-block:: Python


    thresholds, _ = simulator.generate_mu_recruitment_thresholds(
        N=N_motor_units,
        recruitment_range__ratio=recruitment_range,
        deluca__slope=5,
        mode="combined",
    )

    plt.figure()
    plt.plot(thresholds, "o")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_08_simulate_intramuscular_emg_001.png
   :alt: 08 simulate intramuscular emg
   :srcset: /auto_examples/images/sphx_glr_08_simulate_intramuscular_emg_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 70-74

Load Muscle Model
-------------------

Load the **muscle model** with the generated recruitment thresholds.

.. GENERATED FROM PYTHON SOURCE LINES 74-78

.. code-block:: Python


    muscle = joblib.load("results/muscle_model.pkl")
    muscle.length__mm = 30








.. GENERATED FROM PYTHON SOURCE LINES 79-83

Create Intramuscular Electrode Array
------------------------------------

Set up a **differential needle electrode** for intramuscular recordings.

.. GENERATED FROM PYTHON SOURCE LINES 83-94

.. code-block:: Python


    electrode = simulator.IntramuscularElectrodeArray(
        num_electrodes=4,
        inter_electrode_distance__mm=inter_electrode_distance,
        differentiation_mode="consecutive",
        position__mm=electrode_position,
        orientation__rad=(-np.pi / 2, 0, -np.pi / 2),  # perpendicular to muscle
        trajectory_distance__mm=0.125,  # mm
        trajectory_steps=1,  # number of steps
    )








.. GENERATED FROM PYTHON SOURCE LINES 95-99

Initialize Intramuscular EMG Simulator
--------------------------------------

Create the **intramuscular EMG simulator** with the muscle model and electrode.

.. GENERATED FROM PYTHON SOURCE LINES 99-107

.. code-block:: Python


    print("Initializing iEMG simulator...")
    iemg_sim = simulator.IntramuscularEMG(
        muscle_model=muscle,
        electrode_array=electrode,
        MUs_to_simulate=list(range(0, N_motor_units, 3)),
    )





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Initializing iEMG simulator...




.. GENERATED FROM PYTHON SOURCE LINES 108-112

Calculate Motor Unit Action Potentials
--------------------------------------

Compute the **MUAPs** for each motor unit at the electrode positions.

.. GENERATED FROM PYTHON SOURCE LINES 112-119

.. code-block:: Python


    print("Computing motor unit action potentials...")
    iemg_sim.simulate_muaps()
    print(f"  - Generated MUAPs for {N_motor_units} motor units")
    print(f"  - Electrode array with {electrode.num_electrodes} electrodes")






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Computing motor unit action potentials...

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    Computing MUAPs:   0%|          | 0/9 [00:00<?, ?MU/s]
    Computing MUAPs: 100%|██████████| 9/9 [00:00<00:00, 26905.73MU/s]
      - Generated MUAPs for 25 motor units
      - Electrode array with 4 electrodes




.. GENERATED FROM PYTHON SOURCE LINES 120-124

Create Motor Neuron Pool
------------------------

Set up the **motor neuron pool** for spike train generation.

.. GENERATED FROM PYTHON SOURCE LINES 124-129

.. code-block:: Python


    mn_pool = simulator.MotorNeuronPool(recruitment_thresholds=thresholds)
    mvc_current = mn_pool.mvc_current_threshold






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


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    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 105.95pool/s]

    Creating motor neuron pools:   0%|          | 0/1 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 1/1 [00:00<00:00, 89.21pool/s]

    Injecting currents into pools:   0%|          | 0/1 [00:00<?, ?pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 105.73pool/s]

    Creating motor neuron pools:   0%|          | 0/1 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 1/1 [00:00<00:00, 81.77pool/s]

    Injecting currents into pools:   0%|          | 0/1 [00:00<?, ?pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 106.04pool/s]

    Creating motor neuron pools:   0%|          | 0/1 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 1/1 [00:00<00:00, 88.27pool/s]

    Injecting currents into pools:   0%|          | 0/1 [00:00<?, ?pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 104.65pool/s]

    Creating motor neuron pools:   0%|          | 0/1 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 1/1 [00:00<00:00, 88.28pool/s]

    Injecting currents into pools:   0%|          | 0/1 [00:00<?, ?pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 105.51pool/s]

    Creating motor neuron pools:   0%|          | 0/1 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 1/1 [00:00<00:00, 81.72pool/s]

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    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 106.08pool/s]

    Creating motor neuron pools:   0%|          | 0/1 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 1/1 [00:00<00:00, 88.61pool/s]

    Injecting currents into pools:   0%|          | 0/1 [00:00<?, ?pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 1/1 [00:00<00:00, 103.90pool/s]




.. GENERATED FROM PYTHON SOURCE LINES 130-134

Generate Input Currents
-----------------------

Create a **sinusoidal current profile** for the contraction simulation.

.. GENERATED FROM PYTHON SOURCE LINES 134-165

.. code-block:: Python


    n_pools = 2  # Number of distinct motor neuron pools

    timestep = 0.01  # Simulation timestep in ms (high resolution)
    simulation_time = 500  # Total simulation duration in ms

    # Calculate number of time points
    t_points = int(simulation_time / timestep)

    # Create the input current matrix
    sin_amplitudes = [
        mvc_current * 1,
        mvc_current * 1,
    ]  # Same amplitude for both pools
    sin_frequencies = [1.0, 1.0]  # Same frequency for both pools (1 Hz)
    sin_offsets = [
        0.0,
        0.0,
    ]
    sin_phases = [0.0, np.pi]  # 0 and 90 degrees (in radians)

    sinusoidal_currents = create_sinusoidal_current(
        n_pools=n_pools,
        t_points=t_points,
        timestep__ms=timestep,
        amplitudes__muV=sin_amplitudes,
        frequencies__Hz=sin_frequencies,
        offsets__muV=sin_offsets,
        phases__rad=sin_phases,
    )








.. GENERATED FROM PYTHON SOURCE LINES 166-170

Generate Spike Trains
---------------------

Simulate the **neural spike trains** using the motor neuron pool.

.. GENERATED FROM PYTHON SOURCE LINES 170-178

.. code-block:: Python


    print("Generating spike trains...")
    spike_trains, active_indices, _ = mn_pool.generate_spike_trains(
        input_current__matrix=sinusoidal_currents, timestep__ms=timestep
    )
    print(f"  - Generated spike trains for {len(active_indices)} active motor units")
    print(f"  - Total simulation time: {simulation_time} ms")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Generating spike trains...

    Creating motor neuron pools:   0%|          | 0/2 [00:00<?, ?pool/s]
    Creating motor neuron pools: 100%|██████████| 2/2 [00:00<00:00, 90.89pool/s]

    Injecting currents into pools:   0%|          | 0/2 [00:00<?, ?pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools:  50%|█████     | 1/2 [00:00<00:00,  7.88pool/s]

    Adding noise to neurons in pool:   0%|          | 0/25 [00:00<?, ?neuron/s]

                                                                               
    Injecting currents into pools: 100%|██████████| 2/2 [00:00<00:00,  7.88pool/s]
    Injecting currents into pools: 100%|██████████| 2/2 [00:00<00:00,  7.88pool/s]
      - Generated spike trains for 2 active motor units
      - Total simulation time: 500 ms




.. GENERATED FROM PYTHON SOURCE LINES 179-185

Visualize Spike Trains
----------------------

Display the **spike trains** generated by the motor neuron pool in response
to the sinusoidal input current. This shows the neural firing patterns
that will be convolved with the MUAPs to generate the final EMG signal.

.. GENERATED FROM PYTHON SOURCE LINES 185-197

.. code-block:: Python


    spike_fig, spike_ax = plt.subplots(figsize=(10, 6))
    plot_spike_trains(
        spike_trains__matrix=spike_trains,
        timestep__ms=timestep,
        axs=[spike_ax],
        pool_current__matrix=sinusoidal_currents,
        pool_to_plot=[0],  # Show only the first pool
    )
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_08_simulate_intramuscular_emg_002.png
   :alt: Pool 1 Spike Trains
   :srcset: /auto_examples/images/sphx_glr_08_simulate_intramuscular_emg_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 198-202

Simulate Intramuscular EMG
-------------------------

Generate the final **intramuscular EMG signals** by convolving spike trains with MUAPs.

.. GENERATED FROM PYTHON SOURCE LINES 202-216

.. code-block:: Python


    print("Simulating intramuscular EMG signals...")
    emg_signals = iemg_sim.simulate_intramuscular_emg(
        motor_neuron_pool=mn_pool,
    )

    print("Intramuscular EMG simulation completed!")
    print(f"  - EMG signal shape: {emg_signals.shape}")
    print(f"  - Signal RMS (before noise): {np.sqrt(np.mean(emg_signals**2)):.3f} nA")

    print("Adding realistic noise (SNR = 20 dB)...")
    noisy_emg_signals = iemg_sim.add_noise(snr_db=20)
    print(f"  - Signal RMS (after noise): {np.sqrt(np.mean(noisy_emg_signals**2)):.3f} nA")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Simulating intramuscular EMG signals...

    Intramuscular EMG (CPU):   0%|          | 0/2 [00:00<?, ?pool/s]
    Intramuscular EMG (CPU):  50%|█████     | 1/2 [00:00<00:00,  5.58pool/s]
    Intramuscular EMG (CPU): 100%|██████████| 2/2 [00:00<00:00, 11.14pool/s]
    Intramuscular EMG simulation completed!
      - EMG signal shape: (2, 4, 5120)
      - Signal RMS (before noise): 0.153 nA
    Adding realistic noise (SNR = 20 dB)...
      - Signal RMS (after noise): 0.153 nA




.. GENERATED FROM PYTHON SOURCE LINES 217-226

Visualize Intramuscular EMG Results
----------------------------------

Create an **xkcd-style plot** comparing the **intramuscular EMG** signal
with the input current, similar to the surface EMG example.

.. note::
  The intramuscular EMG provides **high spatial resolution** and can detect
  individual motor unit action potentials (MUAPs) with excellent signal quality.

.. GENERATED FROM PYTHON SOURCE LINES 226-280

.. code-block:: Python


    # Clear matplotlib cache and set up xkcd style
    matplotlib.get_cachedir()
    with plt.xkcd():
        plt.rcParams.update({"font.size": 24})

        # Create single plot with normalized signals
        fig, ax = plt.subplots(figsize=(12, 6))

        # Get the signals - use first electrode's differential recording
        iemg_signal = noisy_emg_signals[0, 0, :]  # First electrode differential pair
        current_signal = sinusoidal_currents[0]  # First current pool

        # Create time axes
        emg_time = (
            np.arange(len(iemg_signal)) / iemg_sim.sampling_frequency__Hz
        )  # Convert to seconds
        current_time = mn_pool.times / 1000

        # Normalize iEMG signal by dividing by maximum absolute value
        iemg_normalized = iemg_signal / np.max(np.abs(iemg_signal))

        # Normalize current between 0 and 1
        current_normalized = (current_signal - np.min(current_signal)) / (
            np.max(current_signal) - np.min(current_signal)
        )

    # Plot both normalized signals
    ax.plot(
        emg_time,
        iemg_normalized,
        linewidth=2,
        label="Intramuscular EMG",
    )

    with plt.xkcd():
        ax.plot(
            current_time,
            current_normalized,
            linewidth=2,
            label="Input Current",
        )

        ax.set_xlabel("Time (s)")
        ax.set_ylabel("Normalized Amplitude")
        ax.grid(True, alpha=0.3)
        ax.legend()

        sns.despine(trim=True, left=False, bottom=False, right=True, top=True, offset=5)

        plt.title("Normalized Intramuscular EMG and Input Current")

    plt.tight_layout()
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_08_simulate_intramuscular_emg_003.png
   :alt: Normalized Intramuscular EMG and Input Current
   :srcset: /auto_examples/images/sphx_glr_08_simulate_intramuscular_emg_003.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 21.512 seconds)


.. _sphx_glr_download_auto_examples_08_simulate_intramuscular_emg.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 08_simulate_intramuscular_emg.ipynb <08_simulate_intramuscular_emg.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 08_simulate_intramuscular_emg.py <08_simulate_intramuscular_emg.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 08_simulate_intramuscular_emg.zip <08_simulate_intramuscular_emg.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_