| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Neuroscience | 227 | 60 | 49 | 13 |
The journal covers a variety of subjects, including Neuroscience, Artificial intelligence, Electroencephalography, Artificial neural network and Pattern recognition. Research on Neuroscience presented in the journal focuses, in particular, on Excitatory postsynaptic potential, Neuron, Stimulus (physiology), Stimulation and Hippocampal formation. The journal tackles topics on Excitatory postsynaptic potential, which can potentially contribute to the wider field of Inhibitory postsynaptic potential.
It connects the study in Hippocampal formation with the closely related area of Hippocampus. In addition to Artificial intelligence research, Cognitive Neurodynamics aims to explore topics under Machine learning and Brain–computer interface. Cognitive Neurodynamics explores topics in Electroencephalography which can be helpful for research in disciplines like Speech recognition, Cognition and Audiology.
The research on Artificial neural network featured in Cognitive Neurodynamics combines topics in other fields like Theoretical computer science and Control theory.
The journal publications mostly deal with topics like Artificial intelligence, Neuroscience, Electroencephalography, Artificial neural network and Control theory. While the published articles focused on Artificial intelligence, they were also able to explore topics like Machine learning, Computer vision and Pattern recognition. The journal publications focus on Neuroscience research which is adjacent to topics in Biological system.
The primary areas of discussion in Cognitive Neurodynamics are Artificial intelligence, Electroencephalography, Neuroscience, Pattern recognition and Audiology. While work presented in the journal provided substantial information on Artificial intelligence, it also covered topics in Machine learning, Decoding methods and Brain–computer interface. While Cognitive Neurodynamics focused on Electroencephalography, it was also able to explore topics like Electrophysiology, Alpha (ethology), Functional networks and Cognition.
Cognitive Neurodynamics held discussions to help close the divide between two different fields of study: Neuroscience and Chemistry. While Pattern recognition is the focus of it, it also provided insights into the studies of Artificial neural network and Feature (computer vision). The Audiology works featured in it incorporate elements from Significant difference, Spectral density, Beta (finance), Brain activity and meditation and Attention deficit hyperactivity disorder.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Cognitive Neurodynamics (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Cognitive Neurodynamics (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 2.63% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 31.53% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.01% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.12% of all publications and 42.34% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
The research in Cognitive Neurodynamics also extends to real-world applications. For instance, aspects like Artificial Intelligence, Machine learning, Pattern Recognition can significantly aid in diverse fields such as Speech Pathology. Speech Pathologists utilize these concepts to analyze and treat speech and communication disorders.
For readers interested in using such advanced research in their careers or understanding how these technologies are applied, one practical example is the field of Speech Language Pathology in California. In this state, the profession involves stringent speech pathologist requirements. From having a master's degree in the subject to acquiring a state license, the path is clearly outlined.
The research topics covered by Cognitive Neurodynamics, like Artificial Intelligence, Machine Learning, and Pattern Recognition, have shown their potential to transform this field. They can aid in detecting the speech disorders at an early stage or in developing effective treatment plans. Hence, those interested in this profession or students already studying towards becoming Speech Pathologists, may find the application of these research areas to their field worthy of further exploration.
Lichao Xu;Minpeng Xu;Tzyy-Ping Jung;Tzyy-Ping Jung;Dong Ming
(2021)Cuihua Luo;Cuihua Luo;Fali Li;Peiyang Li;Chanlin Yi
(2021)Cili Zuo;Jing Jin;Erwei Yin;Rami Saab
(2020)Eva Déli;Zoltán Kisvárday
(2020)Andrew A. Fingelkurts;Alexander A. Fingelkurts;Tarja Kallio-Tamminen
(2021)Yangyang Miao;Erwei Yin;Brendan Z Allison;Yu Zhang
(2020)Jiaxin Xie;Lin Jiang;Yanan Li;Baodan Chen
(2021)
