EEG/MEG Signal Processing

Since its invention by the Hans Berger of the electroencepha-lography (EEG) in 1929, it was a strong scientific curiosity in analysis of human brain activity. In fact, the electroen-cephalography (EEG) and magnetoencephalography (MEG) have developed into one of the most important and widely used quantitative diagnostic tools in analysis of brain signals and patterns. EEG and MEG potentially contain a rich source of information related to functional, physiological, and pathological status of the brain. In particularly, they are essential for the identification of mental disorders and brain rhythms extremely useful for the diagnosis and monitoring of brain activity and offer not only the functional but also pathological, physiological, and metabolic changes within the brain and perhaps other parts in the body. Recording and analysis of the EEG and MEG now involve a considerable amount of signal processing for S/N enhancement , feature detection, source localization, automated classification, compression, hidden information extraction, and dynamic modeling. These involve a variety of innovative signal processing methods, including adaptive techniques, time-frequency and timescale procedures, artificial neural networks and fuzzy logic, higher-order statistics and nonlin-ear schemes, fractals, hierarchical trees, Bayesian approaches, and parametric modeling. This special issue contributes to the current status of EEG and MEG signal processing and analysis, with particular regard to recent innovations. It reports some promising achievements by academic and commercial research institutions and individuals, and provides an insight into future developments within this exciting and challenging area of functional brain imaging. Noninvasive functional brain imaging has become an important tool used by neurophysiologists, cognitive psychologists , cognitive scientists, and other researchers interested in brain function. In the last five decades the technology of non-invasive functional imaging has flowered, and researchers today can choose from EEG, MEG, PET, SPECT, MRI, NIRS, and fMRI. Each method has its own strengths and weaknesses. Development of signal processing tools mitigates the problems and alleviates some of the weaknesses. This issue includes the following contributions which cover a wide range of signal processing techniques for analysis , understanding, and recognition of EEG/MEG information. The first paper, " Canonical source reconstruction for MEG " by J. Mattout et al., describes a new, simple but efficient solution to the problem of reconstructing electromagnetic sources into a canonical or standard anatomical space. Electromagnetic lead fields are computed using the warped mesh, in conjunction with a spherical head-model (which does not rely on individual anatomy). The ensuing forward model …