Pub Date : 2024-10-02DOI: 10.1109/TCDS.2024.3471645
Ziming He;Chao Song;Jingchen Li;Haobin Shi
We present progressive diversity reinforcement learning (PDRL), an unsupervised reinforcement learning (URL) method for discovering diverse skills. PDRL encourages learning behaviors that span multiple steps, particularly by introducing “deeper states”—states that require a longer sequence of actions to reach without repetition. To address the challenges of weak skill diversity and weak exploration in partially observable environments, PDRL employs two indications for skill learning to foster exploration and skill diversity, emphasizing each observation and subtrajectory's accuracy compared to its predecessor. Skill latent variables are represented by mappings from states or trajectories, helping to distinguish and recover learned skills. This dual representation promotes exploration and skill diversity without additional modeling or prior knowledge. PDRL also integrates intrinsic rewards through a combination of observations and subtrajectories, effectively preventing skill duplication. Experiments across multiple benchmarks show that PDRL discovers a broader range of skills compared to existing methods. Additionally, pretraining with PDRL accelerates fine-tuning in goal-conditioned reinforcement learning (GCRL) tasks, as demonstrated in Fetch robotic manipulation tasks.
{"title":"PDRL: Towards Deeper States and Further Behaviors in Unsupervised Skill Discovery by Progressive Diversity","authors":"Ziming He;Chao Song;Jingchen Li;Haobin Shi","doi":"10.1109/TCDS.2024.3471645","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3471645","url":null,"abstract":"We present progressive diversity reinforcement learning (PDRL), an unsupervised reinforcement learning (URL) method for discovering diverse skills. PDRL encourages learning behaviors that span multiple steps, particularly by introducing “deeper states”—states that require a longer sequence of actions to reach without repetition. To address the challenges of weak skill diversity and weak exploration in partially observable environments, PDRL employs two indications for skill learning to foster exploration and skill diversity, emphasizing each observation and subtrajectory's accuracy compared to its predecessor. Skill latent variables are represented by mappings from states or trajectories, helping to distinguish and recover learned skills. This dual representation promotes exploration and skill diversity without additional modeling or prior knowledge. PDRL also integrates intrinsic rewards through a combination of observations and subtrajectories, effectively preventing skill duplication. Experiments across multiple benchmarks show that PDRL discovers a broader range of skills compared to existing methods. Additionally, pretraining with PDRL accelerates fine-tuning in goal-conditioned reinforcement learning (GCRL) tasks, as demonstrated in Fetch robotic manipulation tasks.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"495-509"},"PeriodicalIF":5.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1109/TCDS.2024.3470248
Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang
Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing electroencephalography (EEG)-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the nonstationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This article proposes a functional connectivity patterns learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience, and EEG-based emotion recognition technologies.
{"title":"Functional Connectivity Patterns Learning for EEG-Based Emotion Recognition","authors":"Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang","doi":"10.1109/TCDS.2024.3470248","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3470248","url":null,"abstract":"Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing electroencephalography (EEG)-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the nonstationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This article proposes a functional connectivity patterns learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience, and EEG-based emotion recognition technologies.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"480-494"},"PeriodicalIF":5.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1109/TCDS.2024.3470068
Wei Li;Boling Hu;Aiguo Song;Kaizhu Huang
In the field of adversarial games, existing decision-making algorithms primarily rely on reinforcement learning, which can theoretically adapt to diverse scenarios through trial and error. However, these algorithms often face the challenges of low effectiveness and slow convergence in complex wargame environments. Inspired by how human commanders make decisions, this article proposes a novel method named full integration of hierarchical decision-making and tactical knowledge (HDMTK). This method comprises an upper reinforcement learning module and a lower multiagent reinforcement learning (MARL) module. To enable agents to efficiently learn the cooperative strategy, in HDMTK, we separate the whole task into explainable subtasks and devise their corresponding subgoals for shaping the online rewards based on tactical knowledge. Experimental results on the wargame simulation platform “MiaoSuan” show that, compared to the advanced MARL methods, HDMTK exhibits superior performance and faster convergence in the complex scenarios.
{"title":"HDMTK: Full Integration of Hierarchical Decision-Making and Tactical Knowledge in Multiagent Adversarial Games","authors":"Wei Li;Boling Hu;Aiguo Song;Kaizhu Huang","doi":"10.1109/TCDS.2024.3470068","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3470068","url":null,"abstract":"In the field of adversarial games, existing decision-making algorithms primarily rely on reinforcement learning, which can theoretically adapt to diverse scenarios through trial and error. However, these algorithms often face the challenges of low effectiveness and slow convergence in complex wargame environments. Inspired by how human commanders make decisions, this article proposes a novel method named full integration of hierarchical decision-making and tactical knowledge (HDMTK). This method comprises an upper reinforcement learning module and a lower multiagent reinforcement learning (MARL) module. To enable agents to efficiently learn the cooperative strategy, in HDMTK, we separate the whole task into explainable subtasks and devise their corresponding subgoals for shaping the online rewards based on tactical knowledge. Experimental results on the wargame simulation platform “MiaoSuan” show that, compared to the advanced MARL methods, HDMTK exhibits superior performance and faster convergence in the complex scenarios.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"465-479"},"PeriodicalIF":5.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1109/TCDS.2024.3468712
Nikos Piperigkos;Christos Anagnostopoulos;Aris S. Lalos;Petros Kapsalas;Duong Van Nguyen
Simultaneous localization and mapping (SLAM) for positioning of robots and autonomous systems (RASs) and mapping of their surrounding environments is a task of major significance in various applications. However, the main disadvantage of traditional SLAM is that the deployed backend modules suffer from accumulative error caused by sharp viewpoint changes, diverse weather conditions, etc. As such, to improve the localization accuracy of the moving agents, we propose a cost-effective and loosely coupled relocalization backend, deployed on top of original SLAM algorithms, which exploits the topologies of poses and landmarks generated either by camera, LiDAR, or mechanical sensors, to couple and fuse them. This novel fusion scheme enhances the decision-making ability and adaptability of autonomous systems, akin to human cognition, by elaborating graph Laplacian processing concept with Kalman filters. Initially designed for cooperative localization of active road users, this approach optimally combines multisensor information through graph signal processing and Bayesian estimation for self-positioning. Conducted experiments were focused on evaluating how our approach can improve the positioning of autonomous ground vehicles, as prominent examples of RASs equipped with sensing capabilities, in challenging outdoor environments. More specifically, experiments were carried out using the CARLA simulator to generate different types of driving trajectories and environmental conditions, as well as real automotive data captured by an operating vehicle in Langen, Germany. Evaluation study demonstrates that localization accuracy is greatly improved both in terms of overall trajectory error as well as loop closing accuracy for each sensor fusion configuration.
{"title":"Graph-Laplacian-Processing-Based Multimodal Localization Backend for Robots and Autonomous Systems","authors":"Nikos Piperigkos;Christos Anagnostopoulos;Aris S. Lalos;Petros Kapsalas;Duong Van Nguyen","doi":"10.1109/TCDS.2024.3468712","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3468712","url":null,"abstract":"Simultaneous localization and mapping (SLAM) for positioning of robots and autonomous systems (RASs) and mapping of their surrounding environments is a task of major significance in various applications. However, the main disadvantage of traditional SLAM is that the deployed backend modules suffer from accumulative error caused by sharp viewpoint changes, diverse weather conditions, etc. As such, to improve the localization accuracy of the moving agents, we propose a cost-effective and loosely coupled relocalization backend, deployed on top of original SLAM algorithms, which exploits the topologies of poses and landmarks generated either by camera, LiDAR, or mechanical sensors, to couple and fuse them. This novel fusion scheme enhances the decision-making ability and adaptability of autonomous systems, akin to human cognition, by elaborating graph Laplacian processing concept with Kalman filters. Initially designed for cooperative localization of active road users, this approach optimally combines multisensor information through graph signal processing and Bayesian estimation for self-positioning. Conducted experiments were focused on evaluating how our approach can improve the positioning of autonomous ground vehicles, as prominent examples of RASs equipped with sensing capabilities, in challenging outdoor environments. More specifically, experiments were carried out using the CARLA simulator to generate different types of driving trajectories and environmental conditions, as well as real automotive data captured by an operating vehicle in Langen, Germany. Evaluation study demonstrates that localization accuracy is greatly improved both in terms of overall trajectory error as well as loop closing accuracy for each sensor fusion configuration.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"436-453"},"PeriodicalIF":5.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1109/TCDS.2024.3466553
Dawoon Jung;Chengyan Gu;Junmin Park;Joono Cheong
Human–robot collaboration (HRC) has recently attracted increasing attention as a vital component of next-generation automated manufacturing and assembly tasks, yet physical human–robot interaction (pHRI)—which is an inevitable component of collaboration—is often limited to rudimentary touches. This article therefore proposes a deep-learning-based pHRI method that utilizes predefined types of human touch gestures as intuitive communicative signs for collaborative tasks. To this end, a touch gesture network model is first designed upon the framework of the gated recurrent unit (GRU) network, which accepts a set of ground-truth dynamic responses (energy change, generalized momentum, and external joint torque) of robot manipulators under the action of known types of touch gestures and learns to predict the five representative touch gesture types and the corresponding link toward a random touch gesture input. After training the GRU-based touch gesture model using a collected dataset of dynamic responses of a robot manipulator, a total of 35 outputs (five gesture types with seven links each) is recognized with 96.94% accuracy. The experimental results of recognition accuracy correlated with the touch gesture types, and their strength results are shown to validate the performance and disclose the characteristics of the proposed touch gesture model. An example of an IKEA chair assembly task is also presented to demonstrate a collaborative task using the proposed touch gestures. By developing the proposed pHRI method and demonstrating its applicability, we expect that this method can help position physical interaction as one of the key modalities for communication in real-world HRC applications.
{"title":"Touch Gesture Recognition-Based Physical Human–Robot Interaction for Collaborative Tasks","authors":"Dawoon Jung;Chengyan Gu;Junmin Park;Joono Cheong","doi":"10.1109/TCDS.2024.3466553","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3466553","url":null,"abstract":"Human–robot collaboration (HRC) has recently attracted increasing attention as a vital component of next-generation automated manufacturing and assembly tasks, yet physical human–robot interaction (pHRI)—which is an inevitable component of collaboration—is often limited to rudimentary touches. This article therefore proposes a deep-learning-based pHRI method that utilizes predefined types of human touch gestures as intuitive communicative signs for collaborative tasks. To this end, a touch gesture network model is first designed upon the framework of the gated recurrent unit (GRU) network, which accepts a set of ground-truth dynamic responses (energy change, generalized momentum, and external joint torque) of robot manipulators under the action of known types of touch gestures and learns to predict the five representative touch gesture types and the corresponding link toward a random touch gesture input. After training the GRU-based touch gesture model using a collected dataset of dynamic responses of a robot manipulator, a total of 35 outputs (five gesture types with seven links each) is recognized with 96.94% accuracy. The experimental results of recognition accuracy correlated with the touch gesture types, and their strength results are shown to validate the performance and disclose the characteristics of the proposed touch gesture model. An example of an IKEA chair assembly task is also presented to demonstrate a collaborative task using the proposed touch gestures. By developing the proposed pHRI method and demonstrating its applicability, we expect that this method can help position physical interaction as one of the key modalities for communication in real-world HRC applications.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"421-435"},"PeriodicalIF":5.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/TCDS.2024.3465602
SangEun Lee;Seoyun Kim;Yubeen Lee;Jufeng Yang;Eunil Park
Image emotion analysis has gained notable attention owing to the growing importance of computationally modeling human emotions. Most previous studies have focused on classifying the feelings evoked by an image into predefined emotion categories. Compared with these categorical approaches which cannot address the ambiguity and complexity of human emotions, recent studies have taken dimensional approaches to address these problems. However, there is still a limitation in that the number of dimensional datasets is significantly smaller for model training, compared with many available categorical datasets. We propose four types of frameworks that use categorical datasets to predict emotion values for a given image in the valence–arousal (VA) space. Specifically, our proposed framework is trained to predict continuous emotion values under the supervision of categorical labels. Extensive experiments demonstrate that our approach showed a positive correlation with the actual VA values of the dimensional dataset. In addition, our framework improves further when a small number of dimensional datasets are available for the fine-tuning process.
{"title":"Enhancing Dimensional Image Emotion Detection With a Low-Resource Dataset via Two-Stage Training","authors":"SangEun Lee;Seoyun Kim;Yubeen Lee;Jufeng Yang;Eunil Park","doi":"10.1109/TCDS.2024.3465602","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3465602","url":null,"abstract":"Image emotion analysis has gained notable attention owing to the growing importance of computationally modeling human emotions. Most previous studies have focused on classifying the feelings evoked by an image into predefined emotion categories. Compared with these categorical approaches which cannot address the ambiguity and complexity of human emotions, recent studies have taken dimensional approaches to address these problems. However, there is still a limitation in that the number of dimensional datasets is significantly smaller for model training, compared with many available categorical datasets. We propose four types of frameworks that use categorical datasets to predict emotion values for a given image in the valence–arousal (VA) space. Specifically, our proposed framework is trained to predict continuous emotion values under the supervision of categorical labels. Extensive experiments demonstrate that our approach showed a positive correlation with the actual VA values of the dimensional dataset. In addition, our framework improves further when a small number of dimensional datasets are available for the fine-tuning process.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"455-464"},"PeriodicalIF":5.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/tcds.2024.3463194
Xiaoyu Wu, Jiale Liang, Yiang Yu, Guoxin Li, Gary G. Yen, Haoyong Yu
{"title":"Embodied Perception Interaction, and Cognition for Wearable Robotics: A Survey","authors":"Xiaoyu Wu, Jiale Liang, Yiang Yu, Guoxin Li, Gary G. Yen, Haoyong Yu","doi":"10.1109/tcds.2024.3463194","DOIUrl":"https://doi.org/10.1109/tcds.2024.3463194","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"48 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1109/tcds.2024.3462651
Zhendong Guo, Na Dong, Zehui Zhang, Xiaoming Mai, Donghui Li
{"title":"CS-SLAM: A lightweight semantic SLAM method for dynamic scenarios","authors":"Zhendong Guo, Na Dong, Zehui Zhang, Xiaoming Mai, Donghui Li","doi":"10.1109/tcds.2024.3462651","DOIUrl":"https://doi.org/10.1109/tcds.2024.3462651","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"49 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Subcortical regions can be functionally organized into connectivity networks and are extensively communicated with the cortex via reciprocal connections. However, most current research on subcortical networks ignores these interconnections, and networks of the whole brain are of high dimensionality and computational complexity. In this article, we propose a novel cofluctuation-guided subcortical connectivity network construction model based on edge-centric functional connectivity (FC). It is capable of extracting the cofluctuations between the cortex and subcortex and constructing dynamic subcortical networks based on these interconnections. Blind source separation approaches with domain knowledge are designed for dimensionality reduction and feature extraction. Great reproducibility and reliability were achieved when applying our model to two sessions of functional magnetic resonance imaging (fMRI) data. Cortical areas having synchronous communications with the cortex were detected, which was unable to be revealed by traditional node-centric FC. Significant alterations in connectivity patterns were observed when dealing with fMRI of subjects with and without Parkinson's disease, which were further correlated to clinical scores. These validations demonstrated that our model provided a promising strategy for brain network construction, exhibiting great potential in clinical practice.
{"title":"Edge-Centric Functional-Connectivity-Based Cofluctuation-Guided Subcortical Connectivity Network Construction","authors":"Qinrui Ling;Aiping Liu;Taomian Mi;Piu Chan;Xun Chen","doi":"10.1109/TCDS.2024.3462709","DOIUrl":"10.1109/TCDS.2024.3462709","url":null,"abstract":"Subcortical regions can be functionally organized into connectivity networks and are extensively communicated with the cortex via reciprocal connections. However, most current research on subcortical networks ignores these interconnections, and networks of the whole brain are of high dimensionality and computational complexity. In this article, we propose a novel cofluctuation-guided subcortical connectivity network construction model based on edge-centric functional connectivity (FC). It is capable of extracting the cofluctuations between the cortex and subcortex and constructing dynamic subcortical networks based on these interconnections. Blind source separation approaches with domain knowledge are designed for dimensionality reduction and feature extraction. Great reproducibility and reliability were achieved when applying our model to two sessions of functional magnetic resonance imaging (fMRI) data. Cortical areas having synchronous communications with the cortex were detected, which was unable to be revealed by traditional node-centric FC. Significant alterations in connectivity patterns were observed when dealing with fMRI of subjects with and without Parkinson's disease, which were further correlated to clinical scores. These validations demonstrated that our model provided a promising strategy for brain network construction, exhibiting great potential in clinical practice.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"390-399"},"PeriodicalIF":5.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1109/TCDS.2024.3462452
Saydul Akbar Murad;Nick Rahimi
The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It is important to outline this area's recent developments and future research directions to provide a comprehensive understanding of the current state of technology, guide future research efforts, and enhance the effectiveness and accessibility of EEG-to-text systems. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. First, we talk about how EEG-to-text technology has grown and what problems the field still faces. Second, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective brain–computer interface (BCI) technology for a broader user base.
{"title":"Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding Into Text","authors":"Saydul Akbar Murad;Nick Rahimi","doi":"10.1109/TCDS.2024.3462452","DOIUrl":"10.1109/TCDS.2024.3462452","url":null,"abstract":"The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It is important to outline this area's recent developments and future research directions to provide a comprehensive understanding of the current state of technology, guide future research efforts, and enhance the effectiveness and accessibility of EEG-to-text systems. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. First, we talk about how EEG-to-text technology has grown and what problems the field still faces. Second, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective brain–computer interface (BCI) technology for a broader user base.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"61-76"},"PeriodicalIF":5.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：