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 pBOSC, which extends the BOSC (Better OSCillation detection) family of algorithms. Consistent with existing approaches, pBOSC detects oscillatory episodes that exceed both a defined power threshold and a minimum duration criterion. In pBOSC, 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. Using a series of simulated signals, we tested the ability of pBOSC 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 pBOSC's performance on real resting-state magnetoencephalography (MEG) data. By extracting the natural frequency of each brain voxel from the detected oscillatory episodes, we observed a topographic distribution consistent with previous work. In conclusion, pBOSC 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.