Although the electroencephalogram—discovered more than a century ago—has been used for years as a non-invasive diagnostic tool, it is still poorly understood. In this book, John Barlow describes an ingenious new hypothesis for a comprehensive model of the EEG that is able to emulate a large variety of known EEG patterns with few variables.
In contrast to previous hypotheses and models which have treated only selected EEG patterns (rhythmic activity such as alpha activity and sleep spindles seen largely as "filtered noise," or irregular activity, or certain types of epileptiform activity such as spikes) this approach, which is based on an oscillator with two separate input modulations of the extremes and the slopes of waves, covers all types of EEG patterns, and stems from specific features of the EEG itself rather than from arbitrary signals.
Barlow describes the hypothesis in detail, then tests predictions for normal and abnormal EEGs with the aid of a hardware model and with specially developed methods of analysis. The hypothesis is further evaluated in the light of extensive reviews of other EEG models and methods of analysis and of the underlying anatomy, physiology, and pathophysiology of cerebral electrical activity. A technological section details the hardware model and the methodology for testing the hypothesis. Appendixes present some new approaches to traditional methods of EEG analysis and artifact minimization, areas in which Barlow has achieved international recognition.
About the Author
John S. Barlow, M.D., is a Neurophysiologist in the Neurology Service at Massachusetts General Hospital, Senior Research Associate in Neurology (Neurophysiology) at Harvard Medical School, and a Research Affiliate in the Research Laboratory of Electronics at the Massachusetts Institute of Technology.