ArticlePsychophysiology2026
sBOSC: A Method for Source-Level Identification of Neural Oscillations in Electromagnetic Brain Signals.
Article in Psychophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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