A review of deep learning based methods for affect analysis using physiological signals
Emotions are distinct reactions to internal or external events with implications for the
organism. Automatic emotion recognition is a demanding task for pattern recognition and a …
organism. Automatic emotion recognition is a demanding task for pattern recognition and a …
An ensemble learning method for emotion charting using multimodal physiological signals
Emotion charting using multimodal signals has gained great demand for stroke-affected
patients, for psychiatrists while examining patients, and for neuromarketing applications …
patients, for psychiatrists while examining patients, and for neuromarketing applications …
LieWaves: dataset for lie detection based on EEG signals and wavelets
This study introduces an electroencephalography (EEG)-based dataset to analyze lie
detection. Various analyses or detections can be performed using EEG signals. Lie …
detection. Various analyses or detections can be performed using EEG signals. Lie …
An efficient deep learning framework for P300 evoked related potential detection in EEG signal
Background Incorporating the time-frequency localization properties of Gabor transform
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
What multimodal components, tools, dataset and focus of emotion are used in the current research of multimodal emotion: a systematic literature review
R Rahmalina, W Gunawan - Cogent Social Sciences, 2024 - Taylor & Francis
Emotional engagement is essential in human communication, and the meaning of emotions
often entails multimodal relationships. Besides language, multimodality and emotions are …
often entails multimodal relationships. Besides language, multimodality and emotions are …
Tactile Gloves Predict Load Weight During Lifting With Deep Neural Networks
Overexertion in lifting tasks is one of the leading causes of occupational injuries. The load
weight is the key information required to evaluate the risk of a lifting task. However, weight …
weight is the key information required to evaluate the risk of a lifting task. However, weight …
Predicting 3D printed plastic part properties: A deep learning approach with thermographic and vibration data fusion
AS Khusheef, M Shahbazi, R Hashimi - Expert Systems with Applications, 2024 - Elsevier
Additive Manufacturing (AM) holds transformative potential for the manufacturing industry,
yet its widespread adoption is hindered by inconsistent product properties. This study …
yet its widespread adoption is hindered by inconsistent product properties. This study …
Classification of human emotional states based on valence-arousal scale using electroencephalogram
GSS Kumar, N Sampathila… - Journal of Medical Signals …, 2023 - journals.lww.com
Recognition of human emotion states for affective computing based on
Electroencephalogram (EEG) signal is an active yet challenging domain of research. In this …
Electroencephalogram (EEG) signal is an active yet challenging domain of research. In this …
Analysis of brain areas in emotion recognition from eeg signals with deep learning methods
Emotion recognition technology is widely employed in areas such as brain-computer
interfaces, healthcare, security, e-commerce, education, and entertainment. This technology …
interfaces, healthcare, security, e-commerce, education, and entertainment. This technology …
Speech emotion recognition using overlapping sliding window and Shapley additive explainable deep neural network
Speech emotion recognition (SER) has several applications, such as e-learning, human-
computer interaction, customer service, and healthcare systems. Although researchers have …
computer interaction, customer service, and healthcare systems. Although researchers have …