Progress in EEG: Multi-subject Decomposition and Other Advanced Signal Processing Approaches

Electroencephalography (EEG) is generally considered a well-established technique that has extensively been applied to study brain function in health and disease. EEG has been tremendously successful in shaping our understanding of the building blocks of cognition, and how those differ across experimental contexts or between groups of individuals. One major obstacle in the interpretation of EEG, however, is its notoriously low spatial resolution. Electrical currents caused by a multitude of synchronously active neural generators travel through the brain, guided by local conductivity differences of the tissue, pass the skull and finally are registered at relatively wide-spaced electrodes attached to the skin. Care has to be taken when interpreting certain phenomena at selected electrodes across subjects, because already minor differences in brain morphology or generator constellations can obscure or bias actual neural differences (or the lack thereof). Inverse modeling of EEG thus tries to trace the electric potentials measured at the surface of the scalp back to their origins within the brain. To solve this illposed problem, a number of mathematical constraints have to be introduced that may (to a certain degree) be derived from the physical characteristics of neural generators and current flows. EEG inverse modeling in itself is an established and active research field, yet one of its major limitations is its predominant reliance on the relatively sparse spatial information of EEG. More recent procedures, largely driven by machine learning applications to neuroscience data, instead exploit the much richer information found in EEG’s temporal domain. Algorithms for blind source separation, such as independent component analysis (ICA), try to decompose the manifest EEG recordings into its constituent source signals, which then correspond to activity patterns of single regions or coherent neural networks. These techniques are often applied to the data of single subjects. Most EEG researchers will have used ICA for the removal of eye activity, for example, but a growing number of studies use these decompositions to study the latent structure of EEG itself. Methods for the group-level decomposition of EEG data, thus techniques that directly infer the latent structure common across data sets of multiple subjects, are tailored towards solving this exact problem. In functional magnetic resonance imaging, techniques for group-level or multi-subject decomposition have been extremely successful for the study of brain networks and their dynamics at rest or during cognitively demanding tasks, in both healthy as well as clinical populations. Their adaptation and application to EEG data, also extremely powerful and promising, constitutes a rather recent development and is the topic of this collection of articles. This special issue highlights work on both the technical aspects as well as the application of techniques for multi-subject data decomposition of EEG data. Our major aim is to stimulate this field by encouraging and supporting researchers to apply these techniques even though they may not consider themselves methodological experts, while likewise providing more specialist knowledge to interested methodologists to advance the algorithmic development. But the field has meanwhile also advanced with respect to many other techniques for EEG signal processing, and we would like to take the opportunity to highlight some of these as well. The first contribution by Huster and Raud (2017) specifically targets researchers with only limited experience in * René J. Huster rene.huster@psykologi.uio.no