.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "generated/tutorials/stimulation/precise_audio.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_generated_tutorials_stimulation_precise_audio.py: .. _tut-precise-audio: Precise audio presentation ========================== .. include:: ../../../links.inc On the MEG platform, delivering audio stimuli can be done through 2 systems: - :ref:`devices/audio/natus:Natus TIP-300`: most commonly used, it offers excellent sound quality. - :ref:`devices/audio/soundpixx:SoundPixx`: the highs are lower than the :ref:`devices/audio/natus:Natus TIP-300` while the bass are stronger. In both cases, the sound comes from the :ref:`stimulation PC ` and is delivered through the `Crimson 3`_, a USB-audio interface (recognized as a sound card by the :ref:`stimulation PC `). .. note:: If `E-Prime`_ is used, the sound can be delivered through the `Chronos`_. In this case, the signal reaches the `Crimson 3`_ through the RCA connectors. We will not discuss this configuration here. Python sleep precision ---------------------- The :func:`time.sleep` function from the `time` module is not precise enough to halt the program execution for a precise amount of time. The variability depends on the operating system and python version and should be measured before using it in an experiment. .. GENERATED FROM PYTHON SOURCE LINES 33-45 .. code-block:: Python import time import timeit import numpy as np times = timeit.repeat("time.sleep(0.005)", repeat=3, number=100, globals=globals()) times = np.array(times) / 100 print(f"Mean: {np.mean(times):.8f} seconds") print(f"Standard Deviation: {np.std(times):.8f} seconds") .. GENERATED FROM PYTHON SOURCE LINES 47-58 Locally, I measured 5.06822 ms ± 61.4 μs per loop (mean ± std. dev. of 3 runs, 100 loop each). If greater sleeping precision is needed, the sleeping period can be cut into segments waited with :func:`time.sleep` and a last segment where a :func:`time.perf_counter` is used to wait for the remaining time. .. note:: Note that using :func:`time.sleep` is important as it gives back the control to the operating system, allowing other processes to run. .. GENERATED FROM PYTHON SOURCE LINES 58-80 .. code-block:: Python def high_precision_sleep(duration: float) -> None: """High precision sleep function.""" start_time = time.perf_counter() assert 0 < duration, "The duration must be strictly positive." while True: elapsed_time = time.perf_counter() - start_time remaining_time = duration - elapsed_time if remaining_time <= 0: break if remaining_time >= 0.0002: time.sleep(remaining_time / 2) times = timeit.repeat( "high_precision_sleep(0.005)", repeat=3, number=100, globals=globals() ) times = np.array(times) / 100 print(f"Mean: {np.mean(times):.8f} seconds") print(f"Standard Deviation: {np.std(times):.8f} seconds") .. GENERATED FROM PYTHON SOURCE LINES 81-91 Locally, I measured 5.00070 ms ± 0.08 μs per loop (mean ± std. dev. of 3 runs, 100 loop each). Psychopy and Psychtoolbox ------------------------- `Psychtoolbox`_ is a compiled toolbox for MATLAB that allows to present stimuli with precision. Despite a lack of funding and resources to support it, it's still one of the most used stimuli presentation toolboxes. Some of its functionalities are available in Python, especially through the `Psychopy`_ library. .. GENERATED FROM PYTHON SOURCE LINES 91-113 .. code-block:: Python # In this example, we will use the python interface of `Psychtoolbox`_ combined with # triggers from :class:`byte_triggers.ParallelPortTrigger` to deliver synchronize audio # stimuli and triggers. import psychtoolbox as ptb from byte_triggers import ParallelPortTrigger from psychopy.sound.backend_ptb import SoundPTB # create the sound and trigger object sound = SoundPTB(value=440.0, secs=0.2, blockSize=16) trigger = ParallelPortTrigger(0x2FB8) # or "/dev/parport0" on linux # loop 10 times and deliver 10 sounds for k in range(10): now = ptb.GetSecs() sound.play(when=now + 0.2) # schedule the sound in 200 ms high_precision_sleep(0.2) # wait for 200 ms trigger.signal(1) # send the trigger print(f"Sound {k + 1} delivered.") high_precision_sleep(0.5) # wait between sounds .. GENERATED FROM PYTHON SOURCE LINES 114-135 The key elements are to schedule the sound with the ``when`` argument of the `Psychtoolbox`_ backend, wait for the scheduling duration, and deliver the trigger. With this method, the trigger to sound delay should be less than 1 ms. .. figure:: ../../../_static/stimulation-tutorials/trigger-sound-delay-epochs.png :align: center :alt: Epochs showing the trigger to sound delay The measure above was done with a 1024 Hz sampling rate on an ANT Neuro EEG system. Measuring algorithmically the delay between the trigger and the sound is possible through a threshold on the absolute value of the hilbert transformed signal, yielding: .. figure:: ../../../_static/stimulation-tutorials/trigger-sound-delay-hist.png :align: center :alt: Epochs showing the trigger to sound delay .. note:: Note that the measure through the absolute value of the hilbert transformed signal is a good automatic sound onset detection method, but it's not perfect and the thresholding inherently adds jitter to the measure. **Estimated memory usage:** 0 MB .. _sphx_glr_download_generated_tutorials_stimulation_precise_audio.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: precise_audio.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: precise_audio.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: precise_audio.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_