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

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Bibliographic Details
Title: sBOSC: A Method for Source‐Level Identification of Neural Oscillations in Electromagnetic Brain Signals.
Authors: Stern, Enrique (AUTHOR), Niso, Guiomar (AUTHOR), Capilla, Almudena (AUTHOR)
Source: Psychophysiology. Jun2026, Vol. 63 Issue 6, p1-15. 15p.
Subjects: Magnetoencephalography, Brain function localization, Brain waves, Signal detection, Functional connectivity, Neurosciences, Electrophysiology
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. Impact Statement: sBOSC is a method to detect oscillatory episodes at their brain sources that was validated using simulations and real MEG data. Identifying oscillations at their sources enhances brain signals interpretability. The application of pBOSC also allows the optimization of connectivity analyses performed across brain voxels. It also provides alternative metrics for determining the existence of connectivity between two sources. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
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. Impact Statement: sBOSC is a method to detect oscillatory episodes at their brain sources that was validated using simulations and real MEG data. Identifying oscillations at their sources enhances brain signals interpretability. The application of pBOSC also allows the optimization of connectivity analyses performed across brain voxels. It also provides alternative metrics for determining the existence of connectivity between two sources. [ABSTRACT FROM AUTHOR]
ISSN:00485772
DOI:10.1111/psyp.70345