EEG Signal Processing (eBook)

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 for abnormality 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.