Cognitive Neurodynamics

Aims & Scope

Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models.

The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results.

In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome.

The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences.

Papers that are appropriate for non-specialist readers are encouraged. 1.

There is no page limit for manuscripts submitted to Cognitive Neurodynamics.

Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2.

Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication.

Brief Communications should consist of approximately four manuscript pages. 3.

Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey.

There are no restrictions on the number of pages.

Review articles are usually invited, but submitted reviews will also be considered.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.9
2024	3.10

SJR (SCImago Journal Rank)

Year	Value
2024	0.805

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	56

Journal Rank

Year	Value
2024	6729

Journal Citation Indicator

Year	Value
2024	1424

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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
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Neuroscience, 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.

• Dynamic causal modelling for EEG and MEG

  Citation: 179

  Authors: Stefan J., Marta I., Rosalyn J., Karl J.

• EEG classification of driver mental states by deep learning

  Citation: 178

  Authors: Hong, Chen, Guojun, Feiwei, Jianhai, Wanzeng

• EEG-based emotion recognition using 4D convolutional recurrent neural network

  Citation: 174

  Authors: Fangyao, Guojun, Guang, Jianhai, Wanzeng, Hong

• Simulated power spectral density (PSD) of background electrocorticogram (ECoG)

  Citation: 173

  Authors: Walter J., Jian

• Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays

  Citation: 173

  Authors: Xinsong, Jinde, Wenwu

• Power spectral density and coherence analysis of Alzheimer’s EEG

  Citation: 169

  Authors: Ruofan, Jiang, Haitao, Xile, Chen, Bin

• Complex networks and deep learning for EEG signal analysis

  Citation: 164

  Authors: Zhongke, Weidong, Xinmin, Xiaolin, Linhua, Kai, Matjaž

• Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

  Citation: 140

  Authors: Abdolkarim, Maryam, Arash, Ahmad

• Investigation of EEG abnormalities in the early stage of Parkinson’s disease

  Citation: 135

  Authors: Chun-Xiao, Jiang, Guo-Sheng, Yan-Qiu

• An improved localization algorithm based on genetic algorithm in wireless sensor networks

  Citation: 125

  Authors: Bo, Lei

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