Brain Signal Analysis (Paperback)

Preface
With the rise of functional magnetic resonance imaging in the 1990s, it was not uncommon to hear the opinion that cognitive electrophysiology was now doomed to obsolescence. In looking back now, however, it seems that if anything, the advent of fMRI actually stimulated growth in ERPs/EEG and MEG, as they have proved to be complimentary methods to fMRI rather than redundant ones. As a consequence, not only have these measures been taken up by ever greater numbers of researchers, but the tools and techniques of data acquisition and analysis have been evolving at an accelerating pace. The advances being made in this latter regard have been consider- able, and thus, only a few years on since editing a volume on the basics of ERP meth- odology, it seemed that a new book was warranted to capture and disseminate these broader developments in cognitive electrophysiology.
In introducing the book's material, perhaps what the contributions here highlight best is the increasing overlap in EEG and MEG analytic techniques. For example, chapters by Kiebel et al. and Ward and Doesburg concern dynamic causal modeling of evoked responses (chapter 6) and phase synchrony analysis (chapter 7) respectively, both of which are equally applicable to EEG or MEG data. Likewise the beamformer approach to source localization was originally applied to MEG data as discussed by Herdman and Cheyne (chapter 5), yet it has now also been developed for use with EEG signals as well, as detailed by Green and McDonald (chapter 4).
The book begins with a set of chapters speaking to new advances being made in ERP/EEG-related analyses. Among these are Lalor and colleagues' presentation of a novel visual-evoked potential based on reverse correlation methods designed to surmount inherent limitations of the classic visual-evoked potential (chapter 1), Murray and colleagues' new approach to topographic mapping using reference- independent spatial information in high-density electrode montages (chapter 2), and Grave de Peralta Mendez and colleagues' novel method for estimating local field potentials via a newly developed solution for the neuroelectromagnetic inverse problem (chapter 3).