What's happening "under the hood" of the ReceptiveField function from mne.decoding, and can I use this for music beat onsets?

Hi everyone,

Essentially, I want to know how the ReceptiveField function works and if I can apply it for my use case which is decoding imagined music beats using stimulus reconstruction. From what I’ve seen in the docs, you can use this function to reconstruct a speech envelope, but not sure if you can do the same for beat onsets.

My approach was to take my trial events (which are all beat onsets) and attempt to reconstruct these beat onsets using the trial_events object as a parameter in the ReceptiveField function which was a terrible approach. So now I’m looking for more information about this function and how to approach it.

Thank you

Hi Ahnaaf,

It’s not a trivial question to answer succinctly, and I really suggest you spend some time reading through some of the many references on the topic before you delve into this. I highly recommend the refs [1], [4] and [10] as they are really written in a didactic manner and cover a lot of ground.

Regarding your question, I tend to look at it this way : because it’s a linear method the stimulus reconstruction method (aka Backward TRF) method will be able to reconstruct the stimulus only if your stimulus evokes reliable, or repeatable responses in the EEG. So what you really need to ask yourself is whether your biophysiological process of interest (in your case “imagined beats in music”) will trigger reliable responses.

I leave it up to you to answer this, but you might be aware that thew TRF method has been successfully applied to different stimulus features, ranging from low-level features like speech envelope [9], spectrogram [7] or sound intensity [3], to more complex representations of speech like phonemes [2][4][8] or transition probabilities between phonemes [5].

Also note that the linearity hypothesis is reasonable (especially for EEG which has high spatial smearing) and has given interesting results (as you can see from the list of references), but we also know that this assumption is not strictly met and the brain is known to respond in a non-linear way, especially to complex stimuli.


Some References

  1. Brodbeck, C., & Simon, J. Z. (2020). Continuous speech processing. Current Opinion in Physiology , 18 , 25–31. doi:10.1016/j.cophys.2020.07.014
  2. Daube, C., Ince, R. A. A., & Gross, J. (2019). Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech. Current Biology , 29 (12), 1924-1937.e9. doi:10.1016/j.cub.2019.04.067
  3. Drennan, D. P., & Lalor, E. C. (2019). Cortical Tracking of Complex Sound Envelopes: Modeling the Changes in Response with Intensity. Eneuro , 6 (3), ENEURO.0082-19.2019. doi:10.1523/ENEURO.0082-19.2019
  4. Di Liberto, G. M., O’Sullivan, J. A., & Lalor, E. C. (2015). Low-Frequency Cortical Entrainment to Speech Reflects Phoneme-Level Processing. Current Biology , 25 (19), 2457–2465. doi:10.1016/j.cub.2015.08.030
  5. Di Liberto, G. M., Wong, D., Melnik, G. A., & de Cheveigné, A. (2019). Low-frequency cortical responses to natural speech reflect probabilistic phonotactics. NeuroImage , 196 , 237–247. doi:10.1016/j.neuroimage.2019.04.037
  6. Holdgraf, C. R., Rieger, J. W., Micheli, C., Martin, S., Knight, R. T., & Theunissen, F. E. (2017). Encoding and Decoding Models in Cognitive Electrophysiology. Frontiers in Systems Neuroscience , 11 . doi:10.3389/fnsys.2017.00061
  7. Mesgarani, N., & Chang, E. F. (2012). Selective cortical representation of attended speaker in multi-talker speech perception. Nature , 485 (7397), 233–236. doi:10.1038/nature11020
  8. Mesgarani, N., Cheung, C., Johnson, K., & Chang, E. F. (2014). Phonetic Feature Encoding in Human Superior Temporal Gyrus. Science (New York, N.Y.) , 343 (6174), 1006–1010. doi:10.1126/science.1245994
  9. O’Sullivan, J. A., Power, A. J., Mesgarani, N., Rajaram, S., Foxe, J. J., Shinn-Cunningham, B. G., … Lalor, E. C. (2015). Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cerebral Cortex , 25 (7), 1697–1706. doi:10.1093/cercor/bht355
  10. Wong, D. D. E., Fuglsang, S. A., Hjortkjær, J., Ceolini, E., Slaney, M., & de Cheveigné, A. (2018). A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding. Frontiers in Neuroscience , 12 . doi:10.3389/fnins.2018.00531
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