IEEE Transactions on Cognitive and Developmental Systems

Aims & Scope

The IEEE Transactions on Cognitive and Developmental Systems (TCDS) focuses on advances in the study of development and cognition in natural (humans, animals) and artificial (robots, agents) systems.

It welcomes contributions from multiple related disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology.

Articles on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are considered.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	4.9
2024	5.00

Journal Rank

Year	Value
2024	3311

Journal Citation Indicator

Year	Value
2024	2589

SJR (SCImago Journal Rank)

Year	Value
2024	1.249

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	62

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets

  Citation: 291

  Authors: Zirui, Olga, Lipo, Reinhold, Gernot R.

• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016

  Citation: 282

  Authors: Dongrui, Yifan, Bao-Liang

• Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition

  Citation: 267

  Authors: Wei, Jie-Lin, Wei-Long, Bao-Liang

• A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition

  Citation: 258

  Authors: Yang, Lei, Wenming, Yuan, Lei, Zhen, Tong, Tengfei

• Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis

  Citation: 257

  Authors: Wenming

• Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity

  Citation: 238

  Authors: Jinpeng, Shuang, Changde, Yixin, Huiguang

• Deep Reinforcement Learning With Visual Attention for Vehicle Classification

  Citation: 165

  Authors: Dongbin, Yaran, Le

• Decision Making in Multiagent Systems: A Survey

  Citation: 164

  Authors: Yara, Mariette, Edward W.

• Artificial Intelligent System for Automatic Depression Level Analysis Through Visual and Vocal Expressions

  Citation: 163

  Authors: Asim, Hongying, Yona Falinie Binti A., Fan

• Affective EEG-Based Person Identification Using the Deep Learning Approach

  Citation: 159

  Authors: Theerawit, Apiwat, Karis, Tanaboon, Nannapas, Ekapol

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