Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.

Significance: Subcortical structures play a critical role in brain functions such as sensory perception, memory, emotion, and consciousness. There are limited options for assessing neuronal dynamics within subcortical structures in humans. Magnetoencephalography and electroencephalography can measure electromagnetic fields generated by subcortical activity. But localizing the sources of these fields is very difficult, because the fields generated by subcortical structures are small and cannot be distinguished from distributed cortical activity. We show that cortical and subcortical fields can be distinguished if the cortical sources are sparse. We then describe an algorithm that uses sparsity in a hierarchical fashion to jointly localize cortical and subcortical sources. Our work offers alternative perspectives and tools for assessing subcortical brain dynamics in humans.

In order to extract information in a rich environment, we focus on different features that allow us to direct attention to whatever source is of interest. The cortical network deployed during spatial attention, especially in vision, is well characterized. For example, visuospatial attention engages a frontoparietal network including the frontal eye fields (FEFs), which modulate activity in visual sensory areas to enhance the representation of an attended visual object. However, relatively little is known about the neural circuitry controlling attention directed to non-spatial features, or to auditory objects or features (either spatial or non-spatial). Here, using combined magnetoencephalography (MEG) and anatomical information obtained from MRI, we contrasted cortical activity when observers attended to different auditory features given the same acoustic mixture of two simultaneous spoken digits. Leveraging the fine temporal resolution of MEG, we establish that activity in left FEF is enhanced both prior to and throughout the auditory stimulus when listeners direct auditory attention to target location compared to when they focus on target pitch. In contrast, activity in the left posterior superior temporal sulcus (STS), a region previously associated with auditory pitch categorization, is greater when listeners direct attention to target pitch rather than target location. This differential enhancement is only significant after observers are instructed which cue to attend, but before the acoustic stimuli begin. We therefore argue that left FEF participates more strongly in directing auditory spatial attention, while the left STS aids auditory object selection based on the non-spatial acoustic feature of pitch.