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.

Electroencephalograms (EEGs) are becoming increasingly importantmeasurements of brain activity and they have great potential forthe diagnosis and treatment of mental and brain diseases andabnormalities. With appropriate interpretation methods they areemerging as a key methodology to satisfy the increasing globaldemand for more affordable and effective clinical and healthcareservices.Developing and understanding advanced signal processingtechniques for the analysis of EEG signals is crucial in the areaof biomedical research. This book focuses on these techniquesproviding expansive coverage of algorithms and tools from the fieldof digital signal processing. It discusses their applications tomedical data, using graphs and topographic images to showsimulation results that assess the efficacy of the methods.Additionally, expect to find:* explanations of the significance of EEG signal analysis andprocessing (with examples) and a useful theoretical andmathematical background for the analysis and processing of EEGsignals;* an exploration of normal and abnormal EEGs, neurologicalsymptoms and diagnostic information, and representations of theEEGs;* reviews of theoretical approaches in EEG modelling, such asrestoration, enhancement, segmentation, and the removal ofdifferent internal and external artefacts from the EEG and ERP(event-related potential) signals;* coverage of major abnormalities such as seizure, and mentalillnesses such as dementia, schizophrenia, and Alzheimer'sdisease, together with their mathematical interpretations from theEEG and ERP signals and sleep phenomenon;* descriptions of nonlinear and adaptive digital signalprocessing techniques for abnormality detection, sourcelocalization and brain-computer interfacing using multi-channel EEGdata with emphasis on non-invasive techniques, together with futuretopics for research in the area of EEG signal processing.The information within EEG Signal Processing has thepotential to enhance the clinically-related information within EEGsignals, thereby aiding physicians and ultimately providing morecost effective, efficient diagnostic tools. It will be beneficialto psychiatrists, neurophysiologists, engineers, and students orresearchers in neurosciences. Undergraduate and postgraduatebiomedical engineering students and postgraduate epileptologystudents will also find it a helpful reference.