Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is tha...
Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained a...
Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human–computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education,...
This overview examines recent advancements in EEG-based biometric identification, focusing on integrating emotional recognition to enhance the robustness and accuracy of biometric systems. By leveraging the unique physiological properties of EEG sign...
Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies i...
Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differenc...
In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still ro...
Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life—influencing the way we evaluate ourselves, our surroundings, and how we interact with our...
Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recogni...
Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included label...
EEG-based emotion recognition research has become a hot research topic. However, many studies focus on identifying emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals, ignoring the...
Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning m...
Emotion recognition based on the multi-channel electroencephalograph (EEG) is becoming increasingly attractive. However, the lack of large datasets and privacy concerns lead to models that often do not have enough data for training, limiting the rese...
As a major daily task for the popularization of artificial intelligence technology, more and more attention has been paid to the scientific research of mental state electroencephalogram (EEG) in recent years. To retain the spatial information of EEG...
Electroencephalogram (EEG) signals exhibit low amplitude, complex background noise, randomness, and significant inter-individual differences, which pose challenges in extracting sufficient features and can lead to information loss during the mapping...
EEG-based emotion recognition can help achieve more natural human-computer interaction, but the temporal non-stationarity of EEG signals affects the robustness of EEG-based emotion recognition models. Most existing studies use the emotional EEG data...
Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and p...
Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the ra...
Background/Objectives: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to that of unimodal appro...
3 May 2021
Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape...
To address cross-subject domain shift and insufficient complementary fusion of time-frequency information in EEG-based emotion recognition, this paper proposes a multi-task adversarial network: Path Adversarial Dual-Branch Network for EEG Emotion Rec...
Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to...
Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigat...
Emotion recognition is crucial in understanding human affective states with various applications. Electroencephalography (EEG)—a non-invasive neuroimaging technique that captures brain activity—has gained attention in emotion recognition....
Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human–computer interaction (HCI), as it plays an important role in human daily life. Although v...
Electroencephalogram (EEG) is the most preferred and credible source for emotion recognition, where long-short range features and a multichannel relationship are crucial for performance because numerous physiological components function at various ti...
Electroencephalography (EEG)-based eye state recognition often suffers from low accuracy due to noise interference in the collected signals. To address this issue, this study proposes a novel method combining cascaded variational mode decomposition (...
Human emotions are complex psychological and physiological responses to external stimuli. Correctly identifying and providing feedback on emotions is an important goal in human–computer interaction research. Compared to facial expressions, spee...
Affective computing aims to develop systems capable of effectively interacting with people through emotion recognition. Neuroscience and psychology have established models that classify universal human emotions, providing a foundational framework for...
Electroencephalogram (EEG) signals provide a direct and non-invasive means of interpreting brain activity and are increasingly becoming valuable in embedded emotion-aware systems, particularly for applications in healthcare, wearable electronics, and...
The advancement of an intelligent automobile sound switching system has the potential to elevate the market standing of automotive products, with the pivotal prerequisite being the selection of automobile sounds based on the driver’s subjective...
Emotion recognition based on EEG signals remains a challenging task due to the complex spatiotemporal properties of brain activity and substantial intersubject variability. To address these challenges, we propose the EED-CL framework, which integrate...
Although deep learning-based electroencephalography (EEG) emotion recognition methods have achieved promising performance, they generally depend on complex network architectures and high computational costs, which limits their deployment in resource-...
Emotion recognition based on Electroencephalogram (EEG) signals plays a vital role in affective computing and human–computer interaction (HCI). However, noise, artifacts, and signal distortions present challenges that limit classification accur...
Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the...
To improve the accuracy of emotion recognition, this paper proposes a multi-view EEG-fNIRS and cross-attention fusion module named FGCN-TCNN-CAF, which employs a differentiated modeling strategy for the frequency, spatial, and temporal features of EE...
Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, st...
Emotion recognition is a key attribute for realizing advances in human–computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity...
The electroencephalogram (EEG) can objectively reflect the emotional state of human beings, and has attracted much attention in the academic circles in recent years. However, due to its weak, non-stationary, and low signal-to-noise properties, it is...
EEG-based emotion recognition has become an important part of human–computer interaction. To solve the problem that single-modal features are not complete enough, in this paper, we propose a multimodal emotion recognition method based on the at...
Emotion recognition using an odor-induced electroencephalogram (EEG) has broad applications in human-computer interaction. However, existing studies often rely on subjective self-reporting to label emotion, lacking objective verification. While the &...
Emotion analysis is the key technology in human–computer emotional interaction and has gradually become a research hotspot in the field of artificial intelligence. The key problems of emotion analysis based on EEG are feature extraction and cla...
Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognitio...
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of be...
Background: Emotion recognition using electroencephalography (EEG) offers a non-invasive means of measuring brain responses to affective stimuli. However, since EEG signals can vary significantly between subjects, developing a deep learning model cap...
Accurate emotion recognition is crucial for enhancing human–computer interaction, and brain–computer interface (BCI) technology offers an efficient means for emotion detection using the EEG signal. However, existing methods face significa...
Background/Objectives: Adversarial domain adaptation methods are widely used in EEG-based emotion recognition to reduce the influence of individual differences and the non-stationary characteristics of electroencephalogram (EEG) signals. Most existin...
EEG-based emotion recognition (EEG-ER) through deep learning models has gained more attention in recent years, with more researchers focusing on architecture, feature extraction, and generalisability. This paper presents a novel end-to-end deep learn...
To address the challenges of recognition lag and limited representational capacity of single-modal physiological information in driver intention recognition under human–machine co-driving environments, this study constructed a highway driving t...
Emotions are complex phenomena arising from cooperative interactions among multiple brain regions. Electroencephalography (EEG) provides a non-invasive means to observe such neural activity; however, as it captures only electrode-level signals from t...
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