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This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments.
Medicine --- Neurosciences --- consumer behavior --- electroencephalogram (EEG) biosensor --- attention and meditation --- brain computer interface --- Brain-Computer Interface (BCI) --- Steady-State Visual Evoked Potential (SSVEP) --- artefact removal --- Individual Alpha Peak --- movement artefact --- Electroencephalography (EEG) --- classification --- emotion --- facial nerve paralysis --- LASSO --- MEG --- passive brain–computer interface (pBCI) --- EEG headsets --- daily life applications --- In-ear EEG --- echo state network (ESN) --- attention monitoring --- vigilance task --- brain-computer interface (BCI) --- electroencephalography (EEG) --- emotion recognition --- independent component analysis (ICA) --- regression --- stroke --- electroencephalogram (EEG) --- bispectrum --- multimodal fusion --- brain–computer interface (BCI) --- affective computing --- EEG-based emotion detection --- spiking neural network --- NeuCube
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Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data.
Technology: general issues --- Energy industries & utilities --- cardiopulmonary resuscitation (CPR) --- electroencephalogram (EEG) --- hemodynamic data --- carotid blood flow (CBF) --- cerebral circulation --- frequency-shift keying radar --- cross-correlation --- envelope detection --- continuous-wave radar --- frequency discrimination --- vital-signs monitoring --- heartbeat accuracy improvement --- heartbeat detection --- absolute distance measurement --- radar signal processing --- 3D+t modeling --- coronary artery --- non-rigid registration --- cage deformation --- 4D CT --- passenger detection --- CW radar --- radar feature vector --- radar machine learning --- wearable sensors --- physiology --- medical monitoring --- vital signs --- compensatory reserve --- ultra-high resolution --- cone-beam computed tomography --- low-contrast object --- optimal filter --- modulation transfer function --- noise power spectrum --- doppler cardiogram --- wavelet transform --- denoising --- mother wavelet function --- decomposition level --- signal decomposition --- signal-to-noise-ratio
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Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data.
cardiopulmonary resuscitation (CPR) --- electroencephalogram (EEG) --- hemodynamic data --- carotid blood flow (CBF) --- cerebral circulation --- frequency-shift keying radar --- cross-correlation --- envelope detection --- continuous-wave radar --- frequency discrimination --- vital-signs monitoring --- heartbeat accuracy improvement --- heartbeat detection --- absolute distance measurement --- radar signal processing --- 3D+t modeling --- coronary artery --- non-rigid registration --- cage deformation --- 4D CT --- passenger detection --- CW radar --- radar feature vector --- radar machine learning --- wearable sensors --- physiology --- medical monitoring --- vital signs --- compensatory reserve --- ultra-high resolution --- cone-beam computed tomography --- low-contrast object --- optimal filter --- modulation transfer function --- noise power spectrum --- doppler cardiogram --- wavelet transform --- denoising --- mother wavelet function --- decomposition level --- signal decomposition --- signal-to-noise-ratio
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Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions.
self-management interview application --- emotion analysis --- facial recognition --- image-mining --- deep convolutional neural network --- emotion recognition --- pattern recognition --- texture descriptors --- mobile tool --- neuromarketing --- brain computer interface (BCI) --- consumer preferences --- EEG signal --- deep learning --- deep neural network (DNN) --- electroencephalogram (EEG) --- logistic regression --- Gaussian kernel --- Laplacian prior --- affective computing --- human–robot interaction --- thermal IR imaging --- social robots --- facial expression analysis --- line segment feature analysis --- dimensionality reduction --- convolutional recurrent neural network --- driver health risk --- intelligent speech signal processing --- human computer interaction --- supervised learning --- computer vision --- optical flow --- micro facial expressions --- real-time processing --- driver stress state --- IR imaging --- machine learning --- support vector machine (SVR) --- advanced driver-assistance systems (ADAS) --- artificial intelligence --- image processing --- video processing
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Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data.
Technology: general issues --- Energy industries & utilities --- cardiopulmonary resuscitation (CPR) --- electroencephalogram (EEG) --- hemodynamic data --- carotid blood flow (CBF) --- cerebral circulation --- frequency-shift keying radar --- cross-correlation --- envelope detection --- continuous-wave radar --- frequency discrimination --- vital-signs monitoring --- heartbeat accuracy improvement --- heartbeat detection --- absolute distance measurement --- radar signal processing --- 3D+t modeling --- coronary artery --- non-rigid registration --- cage deformation --- 4D CT --- passenger detection --- CW radar --- radar feature vector --- radar machine learning --- wearable sensors --- physiology --- medical monitoring --- vital signs --- compensatory reserve --- ultra-high resolution --- cone-beam computed tomography --- low-contrast object --- optimal filter --- modulation transfer function --- noise power spectrum --- doppler cardiogram --- wavelet transform --- denoising --- mother wavelet function --- decomposition level --- signal decomposition --- signal-to-noise-ratio --- cardiopulmonary resuscitation (CPR) --- electroencephalogram (EEG) --- hemodynamic data --- carotid blood flow (CBF) --- cerebral circulation --- frequency-shift keying radar --- cross-correlation --- envelope detection --- continuous-wave radar --- frequency discrimination --- vital-signs monitoring --- heartbeat accuracy improvement --- heartbeat detection --- absolute distance measurement --- radar signal processing --- 3D+t modeling --- coronary artery --- non-rigid registration --- cage deformation --- 4D CT --- passenger detection --- CW radar --- radar feature vector --- radar machine learning --- wearable sensors --- physiology --- medical monitoring --- vital signs --- compensatory reserve --- ultra-high resolution --- cone-beam computed tomography --- low-contrast object --- optimal filter --- modulation transfer function --- noise power spectrum --- doppler cardiogram --- wavelet transform --- denoising --- mother wavelet function --- decomposition level --- signal decomposition --- signal-to-noise-ratio
Choose an application
Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions.
Information technology industries --- self-management interview application --- emotion analysis --- facial recognition --- image-mining --- deep convolutional neural network --- emotion recognition --- pattern recognition --- texture descriptors --- mobile tool --- neuromarketing --- brain computer interface (BCI) --- consumer preferences --- EEG signal --- deep learning --- deep neural network (DNN) --- electroencephalogram (EEG) --- logistic regression --- Gaussian kernel --- Laplacian prior --- affective computing --- human–robot interaction --- thermal IR imaging --- social robots --- facial expression analysis --- line segment feature analysis --- dimensionality reduction --- convolutional recurrent neural network --- driver health risk --- intelligent speech signal processing --- human computer interaction --- supervised learning --- computer vision --- optical flow --- micro facial expressions --- real-time processing --- driver stress state --- IR imaging --- machine learning --- support vector machine (SVR) --- advanced driver-assistance systems (ADAS) --- artificial intelligence --- image processing --- video processing --- self-management interview application --- emotion analysis --- facial recognition --- image-mining --- deep convolutional neural network --- emotion recognition --- pattern recognition --- texture descriptors --- mobile tool --- neuromarketing --- brain computer interface (BCI) --- consumer preferences --- EEG signal --- deep learning --- deep neural network (DNN) --- electroencephalogram (EEG) --- logistic regression --- Gaussian kernel --- Laplacian prior --- affective computing --- human–robot interaction --- thermal IR imaging --- social robots --- facial expression analysis --- line segment feature analysis --- dimensionality reduction --- convolutional recurrent neural network --- driver health risk --- intelligent speech signal processing --- human computer interaction --- supervised learning --- computer vision --- optical flow --- micro facial expressions --- real-time processing --- driver stress state --- IR imaging --- machine learning --- support vector machine (SVR) --- advanced driver-assistance systems (ADAS) --- artificial intelligence --- image processing --- video processing
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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.
sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer’s disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- n/a --- Alzheimer's disease
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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.
Information technology industries --- sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer's disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer's disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration
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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
Choose an application
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.
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
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