AI Medical Compendium Topic:
Emotions

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Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

Sensors (Basel, Switzerland)
In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-...

EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Sensors (Basel, Switzerland)
Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognit...

FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition.

IEEE journal of biomedical and health informatics
Based on the current research on EEG emotion recognition, there are some limitations, such as hand-engineered features, redundant and meaningless signal frames and the loss of frame-to-frame correlation. In this paper, a novel deep learning framework...

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review.

Sensors (Basel, Switzerland)
Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that...

Deep-Learning-Based Multimodal Emotion Classification for Music Videos.

Sensors (Basel, Switzerland)
Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving effici...

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods.

Sensors (Basel, Switzerland)
Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situa...

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.

Sensors (Basel, Switzerland)
The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is re...

Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.

IEEE transactions on neural networks and learning systems
Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emo...

Emotion Recognition on Edge Devices: Training and Deployment.

Sensors (Basel, Switzerland)
Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost....

Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions.

Sensors (Basel, Switzerland)
Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional s...