Enhanced Time-Locked Decoding for Spoken Words but Not Environmental Sounds in Natural-Like Auditory Conditions.

Overview

1. Department of Neuroscience and Biomedical Engineering Aalto University Espoo Finland
2. BioMag Laboratory, HUS Diagnostic Center Aalto University, University of Helsinki, and Helsinki University Hospital Helsinki Finland

Abstract

Humans are especially sensitive to speech sounds even in complex acoustic environments, but it remains unclear whether speech is tracked differently from other meaningful sounds under such conditions. Magnetoencephalography recordings combined with machine learning have revealed that dynamic time‐locking of cortical activation to unfolding speech is crucial for encoding its acoustic–phonetic features. Here we investigated whether a similar mechanism for speech encoding operates during concurrent processing of speech and nonspeech sounds. Twenty participants listened to superimposed spoken words and nonspeech environmental sounds while attending to one stream at a time. Using a time‐locked decoding model, we reconstructed time‐varying acoustic characteristics of attended and ignored sounds from neural responses in each hemisphere. In the left hemisphere, amplitude envelopes of attended spoken words were decoded significantly better than those of attended environmental sounds at 120‐ to 200‐ms latency between the sound and the cortical activation. No such difference between sound types emerged in the right hemisphere. Furthermore, attention significantly enhanced the decoding of speech sounds in the left hemisphere, suggesting that the observed effects reflect both bottom‐up and top‐down driven processes. These findings imply that particularly the left hemisphere processes speech sounds in a special, time‐locked manner even under adverse auditory conditions. This mechanism may share its neural underpinnings with the previously reported time‐locked tracking of speech amplitude envelope by cortical oscillatory activation or by evoked responses to acoustic edges, supporting efficient extraction of speech features in natural‐like listening conditions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

• 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 0 scripts, each with its path and the digest of its content;
• no match between paragraphs and code yet;
• neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The MEG data cannot be made publicly available due to ethical restrictions imposed by the research ethics committee statement. Relevant derived and pseudonymized data supporting the findings of this study can be shared upon reasonable request and with permission of the research ethics committee for researchers aiming to reproduce the results. Custom code used in the decoding analysis is made openly available at Gitlab (https://version.aalto.fi/gitlab/biomag‐pipelines/cortical_tracking_decoding (https://version.aalto.fi/gitlab/biomag-pipelines/cortical_tracking_decoding)).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

• Publisher: — → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 9 MeSH terms, 4 funders, 65 references.

Cite

This paper

Edström, J., Nora, A., Rinkinen, O., Salmelin, R., & Renvall, H. (2026). Enhanced Time-Locked Decoding for Spoken Words but Not Environmental Sounds in Natural-Like Auditory Conditions. The European journal of neuroscience, 64(1), e70598. https://doi.org/10.1111/ejn.70598

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag.a.1305

Can you <i>feel</i> what I am saying? Speech-based vibrotactile stimulation enhances the cortical tracking of attended speech in a multi-talker background.

Journal: Imaging neuroscience (Cambridge, Mass.)

In common: MEG, cognitive, 12 references

[2] doi:10.1371/journal.pbio.3003924

Endogenous auditory and motor brain rhythms predict individual speech tracking.

Journal: PLoS biology

In common: MEG, cognitive, 10 references

[3] doi:10.1371/journal.pbio.3003876 [code]

Competing speech streams are simultaneously represented in the human cortex during attention switching.

Journal: PLoS biology

In common: cognitive, 10 references

[4] doi:10.1038/s41597-025-06397-4 [code]

MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps and Noisy Sentences

Journal: —

In common: MEG, 8 references

[5] doi:10.1038/s41598-026-57256-0 [code]

Pupil- and gaze dynamics track emotion content in naturalistic speech.

Journal: Scientific reports

In common: cognitive, 8 references

[6] doi:10.1038/s42003-026-10265-1 [code]

The shape of attention reflects flexible filtering of natural speech modulations.

Journal: Communications biology

In common: cognitive, 8 references

[7] doi:10.1093/braincomms/fcag261 [code]

Cortical speech envelope tracking reflects lesion-symptom profiles in post-stroke aphasia.

Journal: Brain communications

In common: cognitive, 7 references

[8] doi:10.1038/s42003-026-10334-5 [code]

The spatially guided cocktail party effect shapes contralateral neural synchronization.

Journal: Communications biology

In common: 7 references

[9] doi:10.1016/j.isci.2026.116458 [code]

Neural tracking of prosodic and statistical rhythms jointly supports artificial language learning.

Journal: iScience

In common: cognitive, 6 references

[10] doi:10.1162/nol.a.249 [code]

How Low-Frequency Neural Activity Structures Language in Time.

Journal: Neurobiology of language (Cambridge, Mass.)

In common: cognitive, 6 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.