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MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-18 Jingwei Tang, Guodao Sun, Jiahui Chen, Gefei Zhang, Baofeng Chang, Haixia Wang, Ronghua Liang
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Minimizing EEG Human Interference: A Study of an Adaptive EEG Spatial Feature Extraction with Deep Convolutional Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-18 Haojin Deng, Shiqi Wang, Yimin Yang, W.G.Will Zhao, Hui Zhang, Ruizhong Wei, Q.M.Jonathan Wu, Bao-Liang Lu
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Towards Two-Stream Foveation-based Active Vision Learning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-17 Timur Ibrayev, Amitangshu Mukherjee, Sai Aparna Aketi, Kaushik Roy
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Cognitive Assessment of Scientific Creative-Skill by Brain-Connectivity Analysis Using Graph Convolutional-Interval Type-2 Fuzzy Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-16 Sayantani Ghosh, Amit Konar, Atulya K. Nagar
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Programmable Bionic Control Circuit Based on Central Pattern Generator IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-12 Qinghui Hong, Qing Li, Jia Li, Jingru Sun, Sichun Du
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Spatio-Temporal Feature Enhancement Network for Blur Robust Underwater Object Detection IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-12 Hao Zhou, Lu Qi, Hai Huang, Xu Yang, Jing Yang
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Unifying Obstacle Avoidance and Tracking Control of Redundant Manipulators Subject to Joint Constraints: A New Data-Driven Scheme IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-11 Peng Yu, Ning Tan, Zhaohui Zhong, Cong Hu, Binbin Qiu, Changsheng Li
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IEEE Transactions on Cognitive and Developmental Systems Information for Authors IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-09
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Deep Learning to Interpret Autism Spectrum Disorder Behind the Camera IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-09 Shi Chen, Ming Jiang, Qi Zhao
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IEEE Transactions on Cognitive and Developmental Systems Publication Information IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-09
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Efficient Semi-Supervised Object Segmentation for Long-Term Videos Using Adaptive Memory Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-08 Shan Zhong, Guoqiang Li, Wenhao Ying, Fuzhou Zhao, Gengsheng Xie, Shengrong Gong
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GENet: A Generic Neural Network for Detecting Various Neurological Disorders from EEG IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-08 Md. Nurul Ahad Tawhid, Siuly Siuly, Kate Wang, Hua Wang
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Guest Editorial Special Issue on Movement Sciences in Cognitive Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-08 Junpei Zhong, Ran Dong, Soichiro Ikuno, Yanan Li, Chenguang Yang
Movements play a critical role in robotic systems, with considerations varying across different robotic systems regarding factors, such as accuracy, speed, energy consumption, and naturalness of movements in various parts of the robotic mechanics. Over the past decades, the robotics community has developed computationally efficient mathematical tools for studying, simulating, and optimizing movements
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Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-02 Yuqi Liu, Qichao Zhang, Yinfeng Gao, Dongbin Zhao
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DatUS: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-02 Sonal Kumar, Arijit Sur, Rashmi Dutta Baruah
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Attention Mechanism and Out-of-Distribution Data on Cross Language Image Matching for Weakly Supervised Semantic Segmentation IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-02 Chi-Chia Sun, Jing-Ming Guo, Chen-Hung Chung, Bo-Yu Chen
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MAT: Morphological Adaptive Transformer for Universal Morphology Policy Learning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-01 Boyu Li, Haoran Li, Yuanheng Zhu, Dongbin Zhao
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BitSNNs: Revisiting Energy-efficient Spiking Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-04-01 Yangfan Hu, Qian Zheng, Gang Pan
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Measuring Human Comfort in Human-Robot Collaboration via Wearable Sensing IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-29 Yuchen Yan, Haotian Su, Yunyi Jia
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Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients with Disorder of Consciousness IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-26 Jiahui Pan, Yangzuyi Yu, Jianhui Wu, Xinjie Zhou, Yanbin He, Yuanqing Li
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EventAugment: Learning Augmentation Policies from Asynchronous Event-based Data IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-22 Fuqiang Gu, Jiarui Dou, Mingyan Li, Xianlei Long, Songtao Guo, Chao Chen, Kai Liu, Xianlong Jiao, Ruiyuan Li
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A Two Stage Foveal Vision Tracker based on Transformer Model IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-18 Guang Han, Jianshu Ma, Ziyang Li, Haitao Zhao
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Emergence of human oculomotor behavior in a cable-driven biomimetic robotic eye using optimal control IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-18 Reza Javanmard Alitappeh, Akhil John, Bernardo Dias, A. John van Opstal, Alexandre Bernardino
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The Inadequacy of Reinforcement Learning from Human Feedback - Radicalizing Large Language Models via Semantic Vulnerabilities IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-18 Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, Malka N. Halgamuge
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Converting Artificial Neural Networks to Ultra-Low-Latency Spiking Neural Networks for Action Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-14 Hong You, Xian Zhong, Wenxuan Liu, Qi Wei, Wenxin Huang, Zhaofei Yu, Tiejun Huang
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EEG-based Auditory Attention Detection with Spiking Graph Convolutional Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-12 Siqi Cai, Ran Zhang, Malu Zhang, Jibin Wu, Haizhou Li
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Robust Perception-based Visual Simultaneous Localization and Tracking in Dynamic Environments IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-28 Song Peng, Teng Ran, Liang Yuan, Jianbo Zhang, Wendong Xiao
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Brain Connectivity Analysis for EEG-based Face Perception Task IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-27 Debashis Das Chakladar, Nikhil R Pal
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D-FaST: Cognitive Signal Decoding with Disentangled Frequency-Spatial-Temporal Attention IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-26 WeiGuo Chen, Changjian Wang, Kele Xu, Yuan Yuan, Yanru Bai, Dongsong Zhang
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DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-26 Zhiwen Xiao, Xin Xu, Huanlai Xing, Bowen Zhao, Xinhan Wang, Fuhong Song, Rong Qu, Li Feng
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Deep Reinforcement Learning with Multi-Critic TD3 for Decentralized Multi-Robot Path Planning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-20 Heqing Yin, Chang Wang, Chao Yan, Xiaojia Xiang, Boliang Cai, Changyun Wei
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Agree to Disagree: Exploring Partial Semantic Consistency against Visual Deviation for Compositional Zero-Shot Learning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-20 Xiangyu Li, Xu Yang, Xi Wang, Cheng Deng
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Compressed Video Anomaly Detection of Human Behavior Based on Abnormal Region Determination IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-20 Lijun He, Miao Zhang, Hao Liu, Liejun Wang, Fan Li
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Editorial IEEE Transactions on Cognitive and Developmental Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02 Huajin Tang
As we usher into the new year of 2024, in my capacity as the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems (TCDS), I am happy to extend to you a tapestry of New Year greetings, may this year be filled with prosperity, success, and groundbreaking achievements in our shared fields.
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Guest Editorial Special Issue on Cognitive Learning of Multiagent Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02 Yang Tang, Wei Lin, Chenguang Yang, Nicola Gatti, Gary G. Yen
The development and cognition of biological and intelligent individuals shed light on the development of cognitive, autonomous, and evolutionary robotics. Take the collective behavior of birds as an example, each individual effectively communicates information and learns from multiple neighbors, facilitating cooperative decision making among them. These interactions among individuals illuminate the
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IEEE Transactions on Cognitive and Developmental Systems Information for Authors IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02
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IEEE Transactions on Cognitive and Developmental Systems Publication Information IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02
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An Electroencephalography-Based Brain-Computer Interface for Emotion Regulation with Virtual Reality Neurofeedback IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-29 Kendi Li, Weichen Huang, Wei Gao, Zijing Guan, Qiyun Huang, Jin-Gang Yu, Zhu Liang Yu, Yuanqing Li
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Depression Detection Using an Automatic Sleep Staging Method with an Interpretable Channel-Temporal Attention Mechanism IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-26 Jiahui Pan, Jie Liu, Jianhao Zhang, Xueli Li, Dongming Quan, Yuanqing Li
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Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-23 Ruiqi Wang, Wonse Jo, Dezhong Zhao, Weizheng Wang, Arjun Gupte, Baijian Yang, Guohua Chen, Byung-Cheol Min
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PLOT: Human-like Push-grasping Synergy Learning in Clutter with One-shot Target Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-22 Xiaoge Cao, Tao Lu, Liming Zheng, Yinghao Cai, Shuo Wang
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Kernel Ridge Regression-based Randomized Network for Brain Age Classification and Estimation IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-18 Raveendra Pilli, Tripti Goel, R Murugan, M Tanveer, P. N. Suganthan
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The Effect of Expressive Robot Behavior on Users’ Mental Effort: A Pupillometry Study IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-15 Marieke van Otterdijk, Bruno Laeng, Diana Saplacan Lindblom, Jim Torresen
Robots are becoming part of our social landscape. Social interaction with humans must be efficient and intuitive to understand because nonverbal cues make social interactions between humans and robots more efficient. This study measures mental effort to investigate what factors influence the intuitive understanding of expressive nonverbal robot motions. Fifty participants were asked to watch, while
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TR-TransGAN: Temporal Recurrent Transformer Generative Adversarial Network for Longitudinal MRI Dataset Expansion IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-08 Chen-Chen Fan, Hongjun Yang, Liang Peng, Xiao-Hu Zhou, Sheng Chen, Zeng-Guang Hou
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MIMo: A Multi-Modal Infant Model for Studying Cognitive Development IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-05 Dominik Mattern, Pierre Schumacher, Francisco M. López, Marcel C. Raabe, Markus R. Ernst, Arthur Aubret, Jochen Triesch
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Reconfiguration of cognitive control networks during a long-duration flanker task IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-05 Jia Liu, Yongjie Zhu, Zheng Chang, Tiina Parviainen, Christian Antfolk, Timo Hämäläinen, Fengyu Cong
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Multiple Instance Learning for Cheating Detection and Localization in Online Examinations IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-05 Yemeng Liu, Jing Ren, Jianshuo Xu, Xiaomei Bai, Roopdeep Kaur, Feng Xia
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Dual-GCL: Dual-Graph Contrastive Learning for Unsupervised Person Re-Identification IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-03 Lin Zhang, Ran Song, Yifan Wang, Qian Zhang, Wei Zhang
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Guest Editorial Special Issue on Hybrid Brain–Computer Collaborative Intelligent System IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-12-14 Edmond Q. Wu, Pengwen Xiong, Aiguo Song, Peter X. Liu
Brain–machine fusion, also known as hybrid intelligence or brain–computer interface (BCI), is considered one of the most promising technologies of the 21st century. Its potential impact spans a wide range of disciplines, including cognitive science, information science, artificial intelligence, biology, neuroscience, and engineering. The research in this field aims to seamlessly integrate biological
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Guest Editorial Special Issue on Emerging Topics on Development and Learning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-12-14 Dingsheng Luo, Angelo Cangelosi, Alessandra Sciutti, Weiwei Wan, Ana Tanevska
This special issue will encompass state-of-the-art research on emerging topics related to development and learning in natural and artificial systems. The primary focus of this special issue is to explore the facets of development and learning from a multidisciplinary perspective by convening researchers from the fields of computer science, robotics, psychology, and developmental studies. We invited
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Modeling Motor Control in Continuous Time Active Inference: A Survey IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-12-04 Matteo Priorelli, Federico Maggiore, Antonella Maselli, Francesco Donnarumma, Domenico Maisto, Francesco Mannella, Ivilin Peev Stoianov, Giovanni Pezzulo
The way the brain selects and controls actions is still widely debated. Mainstream approaches based on optimal control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations.
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Generalized Feature Learning for Detection of Novel Objects IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-25 Jierui Liu, Xilong Liu, Zhiqiang Cao, Junzhi Yu, Min Tan
Few-shot object detection (FSOD) aims at heuristically detecting novel objects with limited labeled data. Typical methods focus on the advanced classifications using the features extracted from common backbones. However, these features are usually base domain-biased, which trap the methods due to insufficient knowledge learned by common backbones. In this correspondence, a novel FSOD network is designed
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Human-Collaborative Artificial Intelligence Along With Social Values in Industry 5.0: A Survey of the State-of-the-Art IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-20 Mahdi Khosravy, Neeraj Gupta, Antoine Pasquali, Nilanjan Dey, Rubén González Crespo, Olaf Witkowski
The expected fifth industrial revolution or Industry 5.0 (I-5.0) is human-centered and concerns societal values, and sustainability. I-5.0 focuses on human and machine coworking by augmenting human-collaborative intelligent robots. The current developments in information communications and the increasing market need for high agility and innovative ways to tailor products urge the world for an I-5.0
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Sequential Learning Network With Residual Blocks: Incorporating Temporal Convolutional Information Into Recurrent Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-17 Dongjing Shan, Kun Yao, Xiongwei Zhang
Temporal convolutional networks (TCNs) have shown remarkable performance in sequence modeling and surpassed recurrent neural networks (RNNs) in a number of tasks. However, performing exceptionally on extremely long sequences remains an obstacle due to the restrained receptive field of temporal convolutions and a lack of forgetting mechanism. Although RNNs can carry state transmission down the full
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GCEN: Multiagent Deep Reinforcement Learning With Grouped Cognitive Feature Representation IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-16 Hao Gao, Xin Xu, Chao Yan, Yixing Lan, Kangxing Yao
In recent years, cooperative multiagent deep reinforcement learning (MADRL) has received increasing research interest and has been widely applied to computer games and coordinated multirobot systems, etc. However, it is still challenging to realize high-solution quality and learning efficiency for MADRL under the conditions of incomplete and noisy observations. To this end, this article proposes an
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EMG-Based Cross-Subject Silent Speech Recognition Using Conditional Domain Adversarial Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-09-18 Yakun Zhang, Huihui Cai, Jinghan Wu, Liang Xie, Minpeng Xu, Dong Ming, Ye Yan, Erwei Yin
Machine learning techniques have achieved great success in electromyography (EMG) decoding, but EMG-based cross-subject silent speech recognition (SSR) received less attention because of its high individual variability. Therefore, this article explores the field of cross-subject SSR to improve the recognition performance of EMG data collected from new subjects. First, this article reports on applying