Electroencephalograms (EEGs) are becoming increasingly important
measurements of brain activity and they have great potential for
the diagnosis and treatment of mental and brain diseases and
abnormalities. With appropriate interpretation methods they are
emerging as a key methodology to satisfy the increasing global
demand for more affordable and effective clinical and healthcare
services.
Developing and understanding advanced signal processing
techniques for the analysis of EEG signals is crucial in the area
of biomedical research. This book focuses on these techniques,
providing expansive coverage of algorithms and tools from the field
of digital signal processing. It discusses their applications to
medical data, using graphs and topographic images to show
simulation results that assess the efficacy of the methods.
Additionally, expect to find: explanations of the significance
of EEG signal analysis and processing (with examples) and a useful
theoretical and mathematical background for the analysis and
processing of EEG signals; an exploration of normal and abnormal
EEGs, neurological symptoms and diagnostic information, and
representations of the EEGs; reviews of theoretical approaches in
EEG modelling, such as restoration, enhancement, segmentation, and
the removal of different internal and external artefacts from the
EEG and ERP (event-related potential) signals; coverage of major
abnormalities such as seizure, and mental illnesses such as
dementia, schizophrenia, and Alzheimer's disease, together with
their mathematical interpretations from the EEG and ERP signals and
sleep phenomenon; descriptions of nonlinear and adaptive digital
signal processing techniques forabnormality detection, source
localization and brain-computer interfacing using multi-channel EEG
data with emphasis on non-invasive techniques, together with future
topics for research in the area of EEG signal processing. The
information within "EEG Signal Processing" has the potential to
enhance the clinically-related information within EEG signals,
thereby aiding physicians and ultimately providing more cost
effective, efficient diagnostic tools. It will be beneficial to
psychiatrists, neurophysiologists, engineers, and students or
researchers in neurosciences. Undergraduate and postgraduate
biomedical engineering students and postgraduate epileptology
students will also find it a helpful reference.
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