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The startle response (response to a loud noise, for instance) is a reflex that is wired into the brain at a very basic level. Although everybody has such a reflex, the strength and quickness of the startle response is modified by a subject's underlying psychoneurological state. The nature of this modification, therefore, is now seen as an accurate, objective measure of very deep neurological processes. This book is a comprehensive volume devoted to startle modification and offers a unique overview of the methods, measurement, physiology and psychology of the phenomenon, particularly modification of the human startle eyeblink. Chapters are written by many of the world's leading investigators in the field and include coverage of elicitation and recording of startle blink, issues in measurement and quantification, the neurophysiological basis of the basic startle response and its modification by attentional and affective processes, psychological processes underlying short and long lead interval modification (including prepulse inhibition), applications of startle modification to the study of psychopathology, including schizophrenia, affective disorders, and psychopathy, developmental processes and relationships with ERPs and behavioural measures of information processing.

Startle reaction. --- Cognitive neuroscience --- Methodology.

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Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions.

Medical equipment & techniques --- movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension --- movement intention --- brain–computer interface --- movement-related cortical potential --- neurorehabilitation --- phonocardiogram --- machine learning --- empirical mode decomposition --- feature extraction --- mel-frequency cepstral coefficients --- support vector machines --- computer aided diagnosis --- congenital heart disease --- statistical analysis --- convolutional neural network (CNN) --- long short-term memory (LSTM) --- emotion recognition --- EEG --- ECG --- GSR --- deep neural network --- physiological signals --- electroencephalography --- Brain-Computer Interface --- multiscale principal component analysis --- successive decomposition index --- motor imagery --- mental imagery --- classification --- hybrid brain-computer interface (BCI) --- home automation --- electroencephalogram (EEG) --- steady-state visually evoked potential (SSVEP) --- eye blink --- short-time Fourier transform (STFT) --- convolution neural network (CNN) --- human machine interface (HMI) --- rehabilitation --- wheelchair --- quadriplegia --- Raspberry Pi --- image gradient --- AMR voice --- Open-CV --- image processing --- acoustic --- startle --- reaction --- response --- reflex --- blink --- mobile --- sound --- stroke --- EMG --- brain-computer interface --- myoelectric control --- pattern recognition --- functional near-infrared spectroscopy --- z-score method --- channel selection --- region of interest --- channel of interest --- respiratory rate (RR) --- Electrocardiogram (ECG) --- ECG derived respiration (EDR) --- auscultation sites --- pulse plethysmograph --- biomedical signal processing --- feature selection and reduction --- discrete wavelet transform --- hypertension