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In 1968 at the University of Illinois, David Cohen made scientific history when he measured a neuronal oscillation known as the alpha rhythm from the brains of four human volunteers. The measurement of the alpha rhythm was not in itself a breakthrough: the German psychiatrist, Hans Berger, had discovered the rhythm decades earlier during his first recordings of human electroencephalography (EEG). What was novel about Cohen's experiment was that it did not measure electrical potentials on the scalp as is the case for EEG. Instead, the measurements were made using a metal coil built to measure magnetic fields. Cohen had become the first person to measure the brain's magnetic field and in doing so had pioneered an entirely new method for measuring the brain's activity: magnetoencephalograpy (MEG).In the intervening decades MEG has grown from a highly specialised method practiced by a small number of researchers to a routine method of human neuroscience available at laboratories across the world. However, as the use of MEG has increased, so too has the number of people encountering MEG for the first time, and this has created a growing need for materials that aid the understanding of MEG for those with no prior experience.

A unified treatment of the generation and analysis of brain-generated electromagnetic fields.In Brain Signals, Risto Ilmoniemi and Jukka Sarvas present the basic physical and mathematical principles of magnetoencephalography (MEG) and electroencephalography (EEG), describing what kind of information is available in the neuroelectromagnetic field and how the measured MEG and EEG signals can be analyzed. Unlike most previous works on these topics, which have been collections of writings by different authors using different conventions, this book presents the material in a unified manner, providing the reader with a thorough understanding of basic principles and a firm basis for analyzing data generated by MEG and EEG.The book first provides a brief introduction to brain states and the early history of EEG and MEG, describes the generation of electromagnetic fields by neuronal activity, and discusses the electromagnetic forward problem. The authors then turn to EEG and MEG analysis, offering a review of linear and matrix algebra and basic statistics needed for analysis of the data, and presenting several analysis methods: dipole fitting; the minimum norm estimate (MNE); beamforming; the multiple signal classification algorithm (MUSIC), including RAP-MUSIC with the RAP dilemma and TRAP-MUSIC, which removes the RAP dilemma; independent component analysis (ICA); and blind source separation (BSS) with joint diagonalization.

Magnetoencephalography (MEG) and electroencephalography (EEG) provide complementary views to the neurodynamics of healthy and diseased human brains. Both methods are totally noninvasive and can track with millisecond temporal resolution spontaneous brain activity, evoked responses to various sensory stimuli, as well as signals associated with the performance of motor, cognitive and affective tasks. MEG records the magnetic fields, and EEG the potentials associated with the same neuronal currents, which however are differentially weighted due to the physical and physiological differences between the methods. MEG is rather selective to activity in the walls of cortical folds, whereas EEG senses currents from the cortex (and brain) more widely, making it harder to pinpoint the locations of the source currents in the brain. Another important difference between the methods is that skull and scalp dampen and smear EEG signals, but do not affect MEG. Hence, to fully understand brain function, information from MEG and EEG should be combined. Additionally, the excellent neurodynamical information these two methods provide can be merged with data from other brain-imaging methods, especially functional magnetic resonance imaging where spatial resolution is a major strength. MEG-EEG Primer is the first-ever volume to introduce and discuss MEG and EEG in a balanced manner side-by-side, starting from their physical and physiological bases and then advancing to methods of data acquisition, analysis, visualization, and interpretation. The authors pay special attention to careful experimentation, guiding readers to differentiate brain signals from various artifacts and to assure that the collected data are reliable. The book weighs the strengths and weaknesses of MEG and EEG relative to one another and to other methods used in systems, cognitive, and social neuroscience. The authors also discuss the role of MEG and EEG in the assessment of brain function in various clinical disorders. The book aims to bring members of multidisciplinary research teams onto equal footing so that they can contribute to different aspects of MEG and EEG research and to be able to participate in future developments in the field.

Magnetoencephalography (MEG) is an exciting brain imaging technology that allows real-time tracking of neural activity, making it an invaluable tool for advancing our understanding of brain function. In this comprehensive introduction to MEG, Peter Hansen, Morten Kringelbach, and Riitta Salmelin have brought together the leading researchers to provide the basic tools for planning and executing MEG experiments, as well as analyzing and interpreting the resulting data. Chapters on the basics describe the fundamentals of MEG and its instrumentation, and provide guidelines for designing experiments and performing successful measurements. Chapters on data analysis present it in detail, from general concepts and assumptions to analysis of evoked responses and oscillatory background activity. Chapters on solutions propose potential solutions to the inverse problem using techniques such as minimum norm estimates, spatial filters and beamformers. Chapters on combinations elucidate how MEG can be used to complement other neuroimaging techniques. Chapters on applications provide practical examples of how to use MEG to study sensory processing and cognitive tasks, and how MEG can be used in a clinical setting. These chapters form a complete basic reference source for those interested in exploring or already using MEG that will hopefully inspire them to try to develop new, exciting approaches to designing and analyzing their own studies. This book will be a valuable resource for researchers from diverse fields, including neuroimaging, cognitive neuroscience, medical imaging, computer modelling, as well as for clinical practitioners.