AIMC Topic: Arousal
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CNN and LSTM-Based Emotion Charting Using Physiological Signals.
Sensors (Basel, Switzerland)
Aug 14, 2020
Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance impr...
Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.
Psychological medicine
Aug 3, 2020
BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and s...
A network model of affective odor perception.
PloS one
Jul 30, 2020
The affective appraisal of odors is known to depend on their intensity (I), familiarity (F), detection threshold (T), and on the baseline affective state of the observer. However, the exact nature of these relations is still largely unknown. We there...
Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network.
Sensors (Basel, Switzerland)
Jun 20, 2020
Emotion recognition plays an important role in the field of human-computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an ...
Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?
Sleep & breathing = Schlaf & Atmung
May 26, 2020
PURPOSE: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually req...
Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Apr 2, 2020
OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.
Decoding dynamic affective responses to naturalistic videos with shared neural patterns.
NeuroImage
Feb 7, 2020
This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight partic...
A Multi-Column CNN Model for Emotion Recognition from EEG Signals.
Sensors (Basel, Switzerland)
Oct 31, 2019
We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a s...
Hybrid scattering-LSTM networks for automated detection of sleep arousals.
Physiological measurement
Jul 23, 2019
OBJECTIVE: Early detection of sleep arousal in polysomnographic (PSG) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications, including heart disease and blood pressure fluctuations.
Deep learning of spontaneous arousal fluctuations detects early cholinergic defects across neurodevelopmental mouse models and patients.
Proceedings of the National Academy of Sciences of the United States of America
Jul 22, 2019
Neurodevelopmental spectrum disorders like autism (ASD) are diagnosed, on average, beyond age 4 y, after multiple critical periods of brain development close and behavioral intervention becomes less effective. This raises the urgent need for quantita...
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