Dr. Cheng Cheng is a lecturer at the Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, with a Ph.D. in Computer Science from Dalian University of Technology (2024). Her interdisciplinary expertise lies in affective computing, neural signal processing, and mental health assessment using EEG data. She leads research integrating spatiotemporal and multimodal analysis for emotion recognition and depression detection. Dr. Cheng is recognized for proposing the SASD-MCL model to enhance EEG-based emotion recognition in scenarios with limited annotations. Her publications appear in reputed journals in machine learning and neuroscience. As a committed educator and lab leader, she mentors students, oversees collaborative projects, and contributes to knowledge dissemination across AI and cognitive science domains. She actively participates in academic forums and maintains professional memberships in cognitive computing and brain research societies. Dr. Cheng’s work stands at the intersection of artificial intelligence and human emotion, contributing to advancements in mental health technologies.
Profile
🎓 Education
Dr. Cheng Cheng received her Ph.D. in Computer Science from Dalian University of Technology in 2024, where her dissertation focused on EEG-based affective computing and mental health applications. During her doctoral studies, she specialized in deep learning, neural signal processing, and cross-domain adaptation models. Her academic training included a rigorous foundation in artificial intelligence, biomedical data analysis, and advanced computational neuroscience. Prior to her Ph.D., she completed her undergraduate and postgraduate studies in Computer Science with distinction, building a strong base in algorithm development and machine learning. Her education journey combined theoretical learning with practical projects and industry collaborations, preparing her for cross-disciplinary research in cognitive science. Through coursework, research assistantships, and conference participations, she gained expertise in cutting-edge neural decoding techniques, emotion modeling, and multimodal data fusion. Dr. Cheng continues to apply her educational background to develop innovative models that bridge brain signal processing and artificial intelligence.
🧪 Experience
Dr. Cheng Cheng is currently serving as a lecturer at the Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, where she also leads a neuroscience and AI-integrated research lab. She has experience supervising postgraduate students, conducting collaborative research, and publishing peer-reviewed work in SCI-indexed journals. Her professional journey includes the development of the SASD-MCL framework for EEG-based emotion recognition and participation in multi-domain research initiatives aimed at improving mental health diagnostics. As a lab leader, she promotes interdisciplinary cooperation between neuroscientists and machine learning experts. Dr. Cheng has participated in national and university-funded research projects and regularly presents at conferences focused on cognitive computing and brain signal interpretation. Her previous roles include research assistantships during her doctoral program, where she refined her expertise in neural signal processing and cross-subject learning models. With a deep interest in innovation, she continues to enhance the accuracy and generalizability of emotion detection systems.
🏅 Awards and Honors
Dr. Cheng Cheng has been recognized for her outstanding contributions to affective computing and brain–AI interfacing. Her model SASD-MCL received academic commendation for significantly improving cross-subject EEG-based emotion recognition, achieving a 5.93% and 5.32% accuracy gain on SEED and SEED-IV datasets, respectively. She has received “Best Paper Presentation” at the International Conference on Cognitive Computing and Neural Interfaces and was awarded a Research Excellence Scholarship during her Ph.D. tenure. Her collaborative work on mental health diagnostics has been featured in top-tier journals, earning her invitations to join editorial boards and review panels. She is an active member of IEEE, the Chinese Association for Artificial Intelligence, and other neuroscience societies. Her leadership in mentoring young researchers and spearheading interdisciplinary projects has also been acknowledged by her institution. Nominated for the “Best Researcher Award,” Dr. Cheng continues to set benchmarks in neural data modeling, emotion AI, and computational mental health technologies.
🔬 Research Focus
Dr. Cheng Cheng’s primary research focus lies in affective computing, neural signal processing, and mental health assessment using EEG data. She integrates deep learning techniques with brain-computer interface (BCI) methodologies to improve the reliability and scalability of emotion recognition systems. Her SASD-MCL model, based on semi-supervised alignment and contrastive learning, addresses key challenges in cross-subject variability and label scarcity. By leveraging spatiotemporal features and multimodal EEG representations, she advances personalized and generalizable emotion detection systems. Her work also explores multi-domain adaptation and knowledge transfer in biomedical signal classification, enhancing robustness under limited supervision. Dr. Cheng’s research bridges neuroscience and artificial intelligence, contributing to innovations in automated mental health screening tools. She is currently involved in projects involving real-time emotion feedback and cognitive state monitoring using portable EEG devices. Her scientific vision aims to foster machine empathy through intelligent systems capable of understanding and responding to human emotions with clinical and social applications.
âś… Conclusion
Dr. Cheng Cheng exemplifies excellence in interdisciplinary research at the intersection of neuroscience and artificial intelligence. Her pioneering contributions to EEG-based emotion recognition and mental health assessment models offer robust, scalable solutions in affective computing. With a strong academic foundation, impactful innovations, and dedicated mentorship, she stands out as a deserving nominee for the Best Researcher Award.
Publications
- EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism
L Feng, C Cheng, M Zhao, H Deng, Y Zhang
IEEE Journal of Biomedical and Health Informatics 26 (11), 5406-5417
- Multimodal emotion recognition using a hierarchical fusion convolutional neural network
Y Zhang, C Cheng, Y Zhang
IEEE access 9, 7943-7951
- Multimodal emotion recognition based on manifold learning and convolution neural network
Y Zhang, C Cheng, YD Zhang
Multimedia Tools and Applications 81 (23), 33253-33268
- Emotion recognition using heterogeneous convolutional neural networks combined with multimodal factorized bilinear pooling
Y Zhang, C Cheng, S Wang, T Xia
Biomedical Signal Processing and Control 77, 103877
- Hybrid network using dynamic graph convolution and temporal self-attention for EEG-based emotion recognition
C Cheng, Z Yu, Y Zhang, L Feng
IEEE Transactions on Neural Networks and Learning Systems
- A novel transformer autoencoder for multi-modal emotion recognition with incomplete data
C Cheng, W Liu, Z Fan, L Feng, Z Jia
Neural Networks 172, 106111
- Multi-domain encoding of spatiotemporal dynamics in EEG for emotion recognition
C Cheng, Y Zhang, L Liu, W Liu, L Feng
IEEE Journal of Biomedical and Health Informatics 27 (3), 1342-1353
- Emotion recognition using hierarchical spatial–temporal learning transformer from regional to global brain
C Cheng, W Liu, L Feng, Z Jia
Neural Networks 179, 106624
- SS-INR: Spatial-spectral implicit neural representation network for hyperspectral and multispectral image fusion
X Wang, C Cheng, S Liu, R Song, X Wang, L Feng
IEEE Transactions on Geoscience and Remote Sensing 61, 1-14
- Multi-channel physiological signal emotion recognition based on relieff feature selection
Y Zhang, C Cheng, T Chen
2019 IEEE 25th International Conference on Parallel and Distributed Systems …
- Dense graph convolutional with joint cross-attention network for multimodal emotion recognition
C Cheng, W Liu, L Feng, Z Jia
IEEE Transactions on Computational Social Systems