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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

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

Enrique Stern¹, Guiomar Niso², Almudena Capilla¹

¹Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain.

Psychophysiology

|June 25, 2026

Summary

We developed sBOSC, a new algorithm to detect neural oscillations directly at their source. This method accurately identifies oscillatory activity in brain signals, improving our understanding of cognitive processes.

Keywords:

EEG MEG aperiodic brain oscillations human motor preparation resting state

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Neural oscillations are crucial for brain function and communication.
Distinguishing true neural oscillations from background noise is a significant methodological challenge.

Purpose of the Study:

To introduce sBOSC, an advanced algorithm for detecting neural oscillations at their source.
To improve the accuracy and reliability of identifying oscillatory activity in electrophysiological data.

Main Methods:

sBOSC extends the Better OSCillation detection (BOSC) algorithm by incorporating source-space analysis and spectral peak identification.
The algorithm detects oscillatory episodes based on power thresholds, duration, and the presence of spatial and spectral peaks.
Validated using simulated data and real magnetoencephalography (MEG) recordings.

Main Results:

sBOSC achieved over 95% accuracy in detecting and localizing simulated neural oscillations under optimal conditions.
Real MEG data analysis revealed accurate topographic distributions of natural frequencies and expected alpha/beta band modulations.
The algorithm successfully identified genuine spectral peaks, differentiating oscillations from aperiodic activity.

Conclusions:

sBOSC provides a novel and effective method for identifying neural oscillations in electrophysiological signals.
By operating in source space and verifying spectral peaks, sBOSC enhances the exploration of brain dynamics.
This advancement offers new possibilities for understanding cognitive processes mediated by neural oscillations.