Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning by Jonathan Axel Cruz-Vazquez, Jesús Yaljá Montiel-Pérez, Rodolfo Romero-Herrera, Elsa Rubio-Espino

“…Affective computing aims to develop systems capable of effectively interacting with people through emotion recognition. …”
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Using Noninvasive Brain Measurement to Explore the Psychological Effects of Computer Malfunctions on Users during Human-Computer Interactions by Leanne M. Hirshfield, Philip Bobko, Alex Barelka, Stuart H. Hirshfield, Mathew T. Farrington, Spencer Gulbronson, Diane Paverman

“…In today’s technologically driven world, there is a need to better understand the ways that common computer malfunctions affect computer users. These malfunctions may have measurable influences on computer user’s cognitive, emotional, and behavioral responses. …”
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Dual-Stage Gated Segmented Multimodal Emotion Recognition Method—***¹ , ***¹ , ***², ***¹

“…Multimodal emotion recognition has broad applications in psychological health monitoring and affective computing. However, most existing studies rely on either global or local features, neglecting the joint modeling of both, which limits performance. …”
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Advances in Video Emotion Recognition: Challenges and Trends by Yun Yi, Yunkang Zhou, Tinghua Wang, Jin Zhou

“…Video emotion recognition (VER), situated at the convergence of affective computing and computer vision, aims to predict the primary emotion evoked in most viewers through video content, with extensive applications in video recommendation, human–computer interaction, and intelligent education. …”
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Speech Emotion Recognition on MELD and RAVDESS Datasets Using CNN by Gheed T. Waleed, Shaimaa H. Shaker

“…Speech emotion recognition (SER) plays a vital role in enhancing human–computer interaction (HCI) and can be applied in affective computing, virtual support, and healthcare. This research presents a high-performance SER framework based on a lightweight 1D Convolutional Neural Network (1D-CNN) and a multi-feature fusion technique. …”
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Personality Trait Prediction From Facial Sketch Leveraged by Expression Muscles by Lifen Weng, Jiangbin Guo, Qibing Zhu

“…These findings, coupled with the newly constructed sketch datasets, offer valuable multimodal resources and methodological insights for researchers in affective computing and behavioral science.…”
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Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis by Ouissale Zaoui Seghroucheni, Mohamed Lazaar, Mohammed Al Achhab

“…Key NLP strategies, including text mining, information extraction, sentiment analysis, clustering, classification, recommendation systems, and affective computing, are evaluated for their effectiveness in identifying and externalizing tacit knowledge. …”
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A multi-dilated convolution network for speech emotion recognition by Samaneh Madanian, Olayinka Adeleye, John Michael Templeton, Talen Chen, Christian Poellabauer, Enshi Zhang, Sandra L. Schneider

“…Abstract Speech emotion recognition (SER) is an important application in Affective Computing and Artificial Intelligence. Recently, there has been a significant interest in Deep Neural Networks using speech spectrograms. …”
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Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts by Fang Li, Dan Zhang

“…The rapid advancement in wearable physiological measurement technology in recent years has brought affective computing closer to everyday life scenarios. Recognizing affective states in daily contexts holds significant potential for applications in human–computer interaction and psychiatry. …”
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Bilingual Dialogue Dataset with Personality and Emotion Annotations for Personality Recognition in Education by Zhi Liu, Yao Xiao, Zhu Su, Luyao Ye, Kaili Lu, Xian Peng

“…It serves as a valuable resource for advancing emotionally intelligent dialogue systems and research in personality and affective computing.…”
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Addressing data scarcity in speech emotion recognition: A comprehensive review by Samuel Kakuba, Dong Seog Han

“…Speech emotion recognition (SER) is a critical field within affective computing, aiming to detect and classify emotional states from speech signals, which vary dynamically over time. …”
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Dual-stage gated segmented multimodal emotion recognition method by MA Fei, LI Shuzhi, YANG Feixia, XU Guangxian

“…Multimodal emotion recognition has broad applications in mental health detection and affective computing. However, most existing methods rely on either global or local features, neglecting the joint modeling of both, which limits emotion recognition performance. …”
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Exploring the Influence of Environmental Characteristics on Emotional Perceptions in Metro Station Spaces by Hedi Shi, Jianfei Chen, Zuhan Feng, Tong Liu, Donghui Sun, Xiaolu Zhou

“…This study adopts an innovative approach by integrating deep learning-based affective computing methods with semantic segmentation techniques in computer vision to systematically evaluate the impact of various physical environmental features and functional spaces on users’ emotional perceptions across multiple dimensions. …”
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Deep convolutional neural network architecture for facial emotion recognition by Dayananda Pruthviraja, Ujjwal Mohan Kumar, Sunil Parameswaran, Vemulapalli Guna Chowdary, Varun Bharadwaj

“…Facial emotion detection is crucial in affective computing, with applications in human-computer interaction, psychological research, and sentiment analysis. …”
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Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition by Hao Chao, Liang Dong, Yongli Liu, Baoyun Lu

“…Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization information across all channels. …”
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A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 by Xuesong Zhai, Xiaoyan Chu, Ching Sing Chai, Morris Siu Yung Jong, Andreja Istenic, Michael Spector, Jia-Bao Liu, Jing Yuan, Yan Li

“…The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). …”
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A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding by Xin Xu, Xinke Shen, Xuyang Chen, Qingzhu Zhang, Sitian Wang, Yihan Li, Zongsheng Li, Dan Zhang, Mingming Zhang, Quanying Liu

“…The EmoEEG-MC dataset serves as a foundational resource for understanding the neural substrates of emotion and enhancing the real-world applicability of affective computing.…”
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A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition by Zarnigor Tagmatova, Sabina Umirzakova, Alpamis Kutlimuratov, Akmalbek Abdusalomov, Young Im Cho

“…Facial expression recognition (FER) plays a critical role in affective computing, enabling machines to interpret human emotions through facial cues. …”
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Cross-subject EEG signals-based emotion recognition using contrastive learning by Ahmed Mohammed Alghamdi, M. Usman Ashraf, Adel A. Bahaddad, Khalid Ali Almarhabi, Waleed A. Al Shehri, Amil Daraz

“…Abstract Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. …”
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Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy by Luis R. Mercado-Diaz, Yedukondala Rao Veeranki, Edward W. Large, Hugo F. Posada-Quintero

“…This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications.…”
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