sBOSC: A Method for Source-Level Identification of Neural Oscillations in Electromagnetic Brain Signals.

Overview

1. Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
2. Cajal Institute & Cajal International Neuroscience Center, CSIC, Madrid, Spain

Abstract

Neural oscillations are recognized as a fundamental component of brain electromagnetic activity. They are implicated in a wide range of cognitive processes and proposed as a core mechanism for brain communication. Nonetheless, detecting genuine neural oscillations remains a methodological challenge, particularly due to the difficulty of distinguishing them from aperiodic background activity. To identify episodes of oscillatory activity directly at their sources, we developed sBOSC, which extends the BOSC (Better OSCillation detection) family of algorithms. Consistent with existing approaches, sBOSC detects oscillatory episodes that exceed both a defined power threshold and a minimum duration criterion. In sBOSC, however, the detection of oscillatory episodes also relies on identifying peaks (i.e., local maxima) in the power spectra as well as throughout the brain volume (spatial peaks). Using a series of simulated signals, we tested the ability of sBOSC to detect and localize oscillations across multiple scenarios. Our results show that most oscillatory episodes were accurately detected at their sources, achieving above 95% accuracy under optimal conditions (i.e., high signal‐to‐noise ratio, lower frequencies, and numerous successive cycles). In addition, we validated sBOSC's performance using real magnetoencephalography (MEG) data from both resting‐state and motor task recordings. From the detected oscillatory episodes, we extracted a topographic distribution of natural frequencies that is consistent with previous work, as well as the expected alpha‐ and beta‐band modulations over sensorimotor regions during motor preparation. In conclusion, sBOSC offers a novel approach for identifying oscillatory activity in electrophysiological signals. It extends previous algorithms by operating in source space and verifying the presence of genuine spectral peaks, thereby enabling new possibilities for exploring brain dynamics.

Reproduced under the paper's license (CC BY-NC), 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.

Code Availability

The full code required to reproduce the method, analyses, and figures in this work is openly available at https://github.com/necog‐UAM/ (https://github.com/necog-UAM/).

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

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

Datasets cited

• humanconnectome.org/study/hcp-young-adult, at Human Connectome Project; found in the references

Data Availability Statement

The data that support the findings of this study are openly available in Open MEG Archive (OMEGA) at https://www.mcgill.ca/bic/neuroinformatics/omega, reference number doi: 10.23686/0015896 (https://doi.org/10.23686/0015896). The dataset containing the resulting oscillatory episodes derived from the OMEGA data is openly available at https://edatos.consorciomadrono.es/dataset.xhtml?persistentId=doi:10.21950/MJYPXG, reference number doi: https://doi.org/10.21950/MJYPXG.

Reproduced under the paper's license (CC BY-NC), 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: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 keywords, 7 MeSH terms, 1 funder, 60 references.

Cite

This paper

Stern, E., Niso, G., & Capilla, A. (2026). sBOSC: A Method for Source-Level Identification of Neural Oscillations in Electromagnetic Brain Signals. Psychophysiology, 63(6), e70345. https://doi.org/10.1111/psyp.70345

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:10.1038/s41598-025-08037-8 [code]

OPM-MEG reveals dynamics of beta bursts underlying attentional processes in sensory cortex

Journal: n/a

In common: MEG, EEG, 8 references

[2] doi:10.1111/ejn.70255 [code]

A Systematic Review of Aperiodic Neural Activity in Clinical Investigations

Journal: n/a

In common: EEG, 6 references

[3] doi:10.1162/netn.a.550 [code]

Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain.

Journal: Network neuroscience (Cambridge, Mass.)

In common: MEG, 6 references

[4] doi:10.1038/s41467-026-73553-8 [code]

Universal rhythmic architecture uncovers two modes of neural dynamics.

Journal: Nature communications

In common: EEG, 6 references

[5] doi:10.1162/imag.a.1255

Beta bursts spatiotemporal profiles and their links to hemodynamic responses during movement and rest.

Journal: Imaging neuroscience (Cambridge, Mass.)

In common: EEG, 6 references

[6] doi:10.1038/s41398-026-04055-w [code]

Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia.

Journal: Translational psychiatry

In common: EEG, 5 references

[7] doi:10.1162/imag.a.1218 [code]

Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm.

Journal: Imaging neuroscience (Cambridge, Mass.)

In common: MEG, 5 references

[8] doi:10.1016/j.isci.2025.113806 [code]

Beta-band frequency shifts signal decisions in human prefrontal cortex

Journal: n/a

In common: 5 references

[9] doi:10.1523/eneuro.0012-26.2026 [code]

I Can't Hear You Both at the Same Time: A Temporal Dilemma During Inter-Regional Communication.

Journal: eNeuro

In common: 4 references

[10] doi:10.1038/s41467-026-71604-8 [code]

Respiration as a dynamic modulator of sensory sampling.

Journal: Nature communications

In common: MEG, 4 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.