Abstract
BrainWave is an easy-to-use Matlab toolbox for the analysis of magnetoencephalography data. It provides a graphical user interface for performing minimum-variance beamforming analysis with rapid and interactive visualization of evoked and induced brain activity. This article provides an overview of the main features of BrainWave with a step-by-step demonstration of how to proceed from raw experimental data to group source images and time series analyses. This includes data selection and pre-processing, magnetic resonance image co-registration and normalization procedures, and the generation of volumetric (whole-brain) or cortical surface based source images, and corresponding source time series as virtual sensor waveforms and their time-frequency representations. We illustrate these steps using example data from a recently published study on response inhibition (Isabella et al., 2015) using the sustained attention to response task paradigm in 12 healthy adult participants. In this task participants were required to press a button with their right index finger to a rapidly presented series of numerical digits and withhold their response to an infrequently presented target digit. This paradigm elicited movement-locked brain responses, as well as task-related modulation of brain rhythmic activity in different frequency bands (e.g., theta, beta, and gamma), and is used to illustrate two different types of source reconstruction implemented in the BrainWave toolbox: (1) event-related beamforming of averaged brain responses and (2) beamformer analysis of modulation of rhythmic brain activity using the synthetic aperture magnetometry algorithm. We also demonstrate the ability to generate group contrast images between different response types, using the example of frontal theta activation patterns during error responses (failure to withhold on target trials). BrainWave is free academic software available for download at http://cheynelab.utoronto.ca/brainwave along with supporting software and documentation. The development of the BrainWave toolbox was supported by grants from the Canadian Institutes of Health Research, the National Research and Engineering Research Council of Canada, and the Ontario Brain Institute.
Highlights
Magnetoencephalography (MEG) involves the measurement of the magnetic fields generated by the electrical currents that flow in activated neuronal circuits of the brain (Hämäläinen et al, 1993; Cheyne and Papanicolaou, 2013)
A major advantage of MEG over other brain imaging methods is the ability to estimate location, strength, and time courses of these neuronal currents by using inverse modeling of electrical brain sources and co-registering such sources to a participant’s own anatomical magnetic resonance image (MRI) – a technique referred to as magnetic source imaging (Cheyne and Papanicolaou, 2013)
For demonstration purposes, we describe the generation of a synthetic aperture magnetometry (SAM) image for the rebound peak
Summary
Magnetoencephalography (MEG) involves the measurement of the magnetic fields generated by the electrical currents that flow in activated neuronal circuits of the brain (Hämäläinen et al, 1993; Cheyne and Papanicolaou, 2013). To avoid the time consuming and subjective process of manual data editing, BrainWave provides epoch rejection features that are automatically applied during single trial or batch epoching.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
