IEEE Transactions on Cognitive and Developmental Systems最新文献

Systematic Benchmarking of Single-Channel EEG for Cognitive State Recognition 基于认知状态识别的单通道脑电图系统基准测试

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-04-16 DOI: 10.1109/TCDS.2026.3683097

Xuyun Wen;Siyuan Wang;Ming Yang;Xia Wu;Daoqiang Zhang

{"title":"Systematic Benchmarking of Single-Channel EEG for Cognitive State Recognition","authors":"Xuyun Wen;Siyuan Wang;Ming Yang;Xia Wu;Daoqiang Zhang","doi":"10.1109/TCDS.2026.3683097","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3683097","url":null,"abstract":"Cognitive state recognition has emerged as a key research topic at the intersection of neuroscience and artificial intelligence. While multichannel electroencephalography (EEG) systems achieve high recognition accuracy, their complexity and cost substantially hinder large-scale deployment. In contrast, single-channel EEG has attracted increasing interest due to its portability and accessibility. Nevertheless, its feasibility for reliable cognitive state recognition remains uncertain, as a single electrode records only limited neural activity. To address this issue, we conducted a systematic set of experiments to comprehensively evaluate the potential of single-channel EEG systems in cognitive state assessment. The evaluation encompasses a broad spectrum of widely adopted tasks, including motor imagery, emotion recognition, and workload estimation. We benchmarked 17 representative EEG analysis approaches, ranging from traditional machine learning algorithms to advanced deep learning models, including methods specifically tailored for single-channel EEG. Furthermore, we introduce a novel quantitative metric to assess electrode placement effectiveness, thereby reducing evaluation bias caused by algorithmic differences. Experimental results reveal that single-channel EEG systems still face considerable challenges in cognitive state recognition. Specifically, their accuracy remains significantly lower than that of multichannel systems, and task performance is highly sensitive to electrode location, with optimal placements exhibiting strong task dependency. These findings suggest that future research should emphasize the development of more effective single-channel EEG analysis techniques and adaptive electrode optimization strategies capable of dynamically adjusting placements according to task requirements.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1341-1354"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149175980","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}

引用次数: 0

IEEE Transactions on Cognitive and Developmental Systems Publication Information IEEE认知与发展系统汇刊

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-10-14 DOI: 10.1109/TCDS.2026.3731645

引用次数: 0

FNIRNet: A Spatiotemporal Statistical Feature-Based Convolutional Neural Network for Low-Channel fNIRS Brain Function Classification 基于时空统计特征的卷积神经网络在低通道近红外脑功能分类中的应用

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-04-16 DOI: 10.1109/TCDS.2026.3684377

Lin Tang;Shuang Zhou;Nan Zhou;Liang Gong;Wenhao Wang;Yue Yu;YengChai Soh;Witold Pedrycz

{"title":"FNIRNet: A Spatiotemporal Statistical Feature-Based Convolutional Neural Network for Low-Channel fNIRS Brain Function Classification","authors":"Lin Tang;Shuang Zhou;Nan Zhou;Liang Gong;Wenhao Wang;Yue Yu;YengChai Soh;Witold Pedrycz","doi":"10.1109/TCDS.2026.3684377","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3684377","url":null,"abstract":"We propose functional near-infrared spectroscopy network (FNIRNet), a lightweight deep learning framework that combines spatiotemporal representations with a statistical feature module to directly process low-channel functional near-infrared spectroscopy signals for brain function classification. A clinically guided and SHAP-validated channel selection strategy identifies 8 critical channels, reducing hardware dependence while preserving discriminative information. On a depression dataset, FNIRNet achieves 92% accuracy and an F1-score of 0.95, outperforming machine-learning baselines including support vector machine, k-nearest neighbors, random forest, and deep models such as electroencephalography network and convolutional neural network–long short-term memory under low-channel input. With 8 channels, FNIRNet also improves computational efficiency and supports practical deployment. The model generalizes to a public stroke dataset, indicating robustness across probe configurations and hemoglobin signals. FNIRNet offers a low-cost, high-performance, and deployable solution for large-scale mental-health screening in nonspecialist settings, and its performance is validated using the area under the receiver operating characteristic curve metric.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1384-1398"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176113","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}

引用次数: 0

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-10-14 DOI: 10.1109/TCDS.2026.3731643

引用次数: 0

Multilevel Multimodal Visual Neural Decoding for Generalized Visual Object Recognition 面向广义视觉对象识别的多层次多模态视觉神经解码

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-03-16 DOI: 10.1109/TCDS.2026.3674164

Wenlong Hang;Junliang Wang;Shuang Liang;Qiong Wang;Guanglin Li;Jing Qin;Badong Chen

{"title":"Multilevel Multimodal Visual Neural Decoding for Generalized Visual Object Recognition","authors":"Wenlong Hang;Junliang Wang;Shuang Liang;Qiong Wang;Guanglin Li;Jing Qin;Badong Chen","doi":"10.1109/TCDS.2026.3674164","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3674164","url":null,"abstract":"The decoding of human visual neural signals has garnered significant attention due to its potential to reveal the underlying cognitive mechanisms of the brain. However, existing visual neural decoding methods mainly focus on instance-level multimodal semantic correspondences, often neglecting local-level and concept-level supervisory information. These limitations substantially weaken their generalizability to downstream tasks. In this study, we propose a multilevel multimodal visual neural decoding framework that leverages multilevel semantic correspondences between paired image stimuli and brain activities to address these limitations. Specifically, we initially employ the instance-level alignment module to capture the correspondence between brain activity–image pairs. Subsequently, a cross-attention mechanism is utilized to learn the matching between tokens from different modalities. The local-level alignment module further exploits the local semantic information based on matched tokens to establish fine-grained local correspondences. To capture concept-level correspondences, we design a momentum update strategy to generate prototypes for different modalities. The concept-level alignment module then utilizes the prototype-based semantic information to capture the concept-level multimodal correspondences. Synergistic learning across multilevel multimodal semantic correspondences significantly enhances the performance of generalized visual object recognition. Extensive experiments conducted on publicly available visual neural datasets demonstrate the superiority of the proposed multilevel multimodal learning framework.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1267-1278"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176037","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}

引用次数: 0

SELFIE-Net: Self-Supervised Learning and Feature-Intensified Emotion Network for Facial Emotion Recognition self - net：用于面部情绪识别的自监督学习和特征强化情绪网络

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-04-07 DOI: 10.1109/TCDS.2026.3681803

Saif Hassan;Ehtesham Hashmi;Ghulam Mujtaba;Muhammad Mudassar Yamin;Muhammad Hussain Mughal;Mohib Ullah

{"title":"SELFIE-Net: Self-Supervised Learning and Feature-Intensified Emotion Network for Facial Emotion Recognition","authors":"Saif Hassan;Ehtesham Hashmi;Ghulam Mujtaba;Muhammad Mudassar Yamin;Muhammad Hussain Mughal;Mohib Ullah","doi":"10.1109/TCDS.2026.3681803","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3681803","url":null,"abstract":"As the requirement for the strong facial emotion recognition (FER) systems in different applications increases, both in healthcare and security, it is important to address issues such as multiple facial expressions and complicated backgrounds. This article proposes a new FER framework called SELFIE-Net that uses “self-supervised learning” combined with “diffusion-based classification head” on both labelled and unlabelled data. The model combines convolutional block attention module (CBAM) to refine the extraction of features. The classification and regression diffusion model (CARD) enhances the prediction of emotional conditions. SELFIE-Net is a modularly integrated system with the power of SSL, and has been applied to bootstrap your own latent (BYOL), Barlow twins, DINO (self-distillation with no labels), and SimCLR to learn the significant features of the unlabeled data. Comprehensive tests on benchmarking datasets such as the FER2013, RAF-DB, and AffectNet show that SELFIE-Net achieves superior performance in comparison to current approaches, establishing new standards in the accuracy and flexibility of FER. This study is not only a contribution to the current body of emotion recognition, but also it shows the possibility of integrating self-supervised learning and attention mechanisms to tackle more complex problems of pattern recognition.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1305-1314"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176229","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}

引用次数: 0

Learning to Gaze: Bioinspired Attention Adaptation Strategy for Social Robots 学习凝视：社交机器人的仿生注意力适应策略

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-02-26 DOI: 10.1109/TCDS.2026.3669012

Khashayar Ghamati;Maryam Banitalebi Dehkordi;Hamed Rahimi Nohooji;Holger Voos;Farshid Amirabdollahian;Abolfazl Zaraki

{"title":"Learning to Gaze: Bioinspired Attention Adaptation Strategy for Social Robots","authors":"Khashayar Ghamati;Maryam Banitalebi Dehkordi;Hamed Rahimi Nohooji;Holger Voos;Farshid Amirabdollahian;Abolfazl Zaraki","doi":"10.1109/TCDS.2026.3669012","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3669012","url":null,"abstract":"Adaptive attention allocation in dynamic social environments remains a fundamental challenge for autonomous robots, requiring the integration of perceptual saliency, social context, and real-time decision-making. We present a bioinspired reinforcement learning framework for robotic gaze control that incorporates a habituation mechanism to regulate the exploration–exploitation trade-off, mirroring how biological attention systems filter redundant stimuli while remaining responsive to novel events. Through a comprehensive ablation study comparing deep Q-learning (DQL), Vanilla Q-learning (VQL), and multiibjective Q-learning (MOL), we uncover a critical insight: habituation significantly enhances DQL performance, improving response efficiency and policy stability, yet causes systematic degradation in MOL due to fundamental incompatibilities between fixed-threshold resets and the extended episodes required for multiobjective optimisation. This differential effect reveals that bioinspired mechanisms cannot be applied universally across learning architectures but must be carefully matched to algorithmic characteristics. Real-world deployment on the ARI humanoid robot validates the framework’s practical applicability, achieving robust gaze prediction accuracy across diverse interaction scenarios with well-calibrated confidence metrics that reliably distinguish correct from incorrect predictions. Our findings provide evidence-based guidelines for integrating biological principles into cognitive robotics, demonstrating both the promise and the pitfalls of bioinspired mechanism design.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1212-1226"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176197","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}

引用次数: 0

Cross-Lingual Attention Distillation With Personality-Informed Generative Augmentation for Multilingual Personality Recognition 基于人格信息生成增强的跨语言注意蒸馏多语言人格识别

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-04-09 DOI: 10.1109/TCDS.2026.3682672

Jing Jie Tan;Ban-Hoe Kwan;Danny Wee-Kiat Ng;Yan-Chai Hum;Noriyuki Kawarazaki;Kosuke Takano;Anissa Mokraoui

{"title":"Cross-Lingual Attention Distillation With Personality-Informed Generative Augmentation for Multilingual Personality Recognition","authors":"Jing Jie Tan;Ban-Hoe Kwan;Danny Wee-Kiat Ng;Yan-Chai Hum;Noriyuki Kawarazaki;Kosuke Takano;Anissa Mokraoui","doi":"10.1109/TCDS.2026.3682672","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3682672","url":null,"abstract":"While significant work has been done on personality recognition, the lack of multilingual datasets remains an unresolved challenge. To address this, we propose ADAM [cross-lingual (a)ttention (d)istillation with personality-informed generative (a)ugmentation for (m)ultilingual personality recognition], a state-of-the-art approach designed to advance multilingual personality recognition. Our approach leverages an existing English-language personality dataset as the primary source and employs a large language model (LLM) for translation-based augmentation, enhanced by personality-informed generative augmentation (PIGA), to generate high-quality training data in multiple languages, including Japanese, Chinese, Malay, and French. We provide a thorough analysis to justify the effectiveness of these augmentation techniques. Building on these advancements, ADAM integrates cross-lingual attention distillation (CLAD) to train a model capable of understanding and recognizing personality traits across languages, bridging linguistic and cultural gaps in personality analysis. This research presents a thorough evaluation of the proposed augmentation method, incorporating an ablation study on recognition performance to ensure fair comparisons and robust validation. Overall, with PIGA augmentation, the findings demonstrate that CLAD significantly outperforms the standard BCE across all languages and personality traits, achieving notable improvements in average BA scores—0.6332 (<inline-formula><tex-math>$+$</tex-math></inline-formula>0.0573) on the Essays dataset and 0.7448 (<inline-formula><tex-math>$+$</tex-math></inline-formula>0.0968) on the Kaggle dataset. The CLAD-trained model also demonstrated strong generalizability and achieved benchmark performance comparable to current leading encoder models.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1325-1340"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149175961","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}

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Search Multilayer Perceptron-Based Fusion for Efficient and Accurate Siamese Tracking 基于多层感知器的搜索融合高效准确的暹罗跟踪

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-03-23 DOI: 10.1109/TCDS.2026.3675813

Tianqi Shen;Huakao Lin;Ning An

{"title":"Search Multilayer Perceptron-Based Fusion for Efficient and Accurate Siamese Tracking","authors":"Tianqi Shen;Huakao Lin;Ning An","doi":"10.1109/TCDS.2026.3675813","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3675813","url":null,"abstract":"Siamese visual trackers have recently advanced through increasingly sophisticated fusion mechanisms built on convolutional or Transformer architectures. However, both struggle to deliver pixel-level interactions efficiently on resource-constrained hardware, leading to a persistent accuracy-efficiency imbalance. Motivated by this limitation, we redesign the Siamese neck with a simple yet effective multilayer perception (MLP)-based fusion module that enables pixel-level interaction with minimal structural overhead. Nevertheless, naïvely stacking MLP blocks introduces a new challenge: computational cost can scale quadratically with channel width. To overcome this, we construct a hierarchical search space of carefully designed MLP modules and introduce a customized relaxation strategy that enables differentiable neural architecture search (DNAS) to decouple channel-width optimization from other architectural choices. This targeted decoupling automatically balances channel width and depth, yielding a low-complexity architecture. The resulting tracker achieves state-of-the-art accuracy-efficiency tradeoffs. It ranks among the top performers on four general-purpose and three aerial tracking benchmarks, while maintaining real-time performance on both resource-constrained graphics processing units (GPUs) and neural processing units (NPUs).","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1279-1291"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176036","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}

引用次数: 0

A User-Guided Framework for Visual Odometry Algorithm Selection and Composition 一种用户导向的视觉里程计算法选择与组成框架

IF 4.7 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2026-10-01 Epub Date: 2026-02-24 DOI: 10.1109/TCDS.2026.3667590

Yanmei Jiao;Zhiming Shen;Yunfeng Lu;Xiumei Li;Zhaowen Xu;Yue Wang;Rong Xiong

{"title":"A User-Guided Framework for Visual Odometry Algorithm Selection and Composition","authors":"Yanmei Jiao;Zhiming Shen;Yunfeng Lu;Xiumei Li;Zhaowen Xu;Yue Wang;Rong Xiong","doi":"10.1109/TCDS.2026.3667590","DOIUrl":"https://doi.org/10.1109/TCDS.2026.3667590","url":null,"abstract":"Visual odometry (VO) plays a critical role in unmanned systems, as its performance directly determines localization accuracy and stability. However, existing VO selection methods often rely on expert knowledge or single-performance metrics, limiting adaptability under diverse user preferences, diverse environments, and device constraints. This article proposes a user-guided framework for VO algorithm selection and composition, named UG-VOS, which models the selection process as a cognitive decision-making system driven by user preferences and environmental context. First, we design a scene-adaptive selection module that embeds lightweight adapters into a transformer encoder to adaptively model environmental features and jointly optimize front-end and back-end algorithm selection. Second, we propose a multiobjective evaluation module that integrates user preferences with environmental context and VO performance to guide algorithm selection, achieving a balanced trade-off among accuracy, efficiency, and system resource usage. Experiments on diverse datasets demonstrate that UG-VOS consistently achieves superior algorithm compositions compared to existing methods, offering a robust trade-off between precision and resource efficiency.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"18 5","pages":"1174-1186"},"PeriodicalIF":4.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176123","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}

引用次数: 0