| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Neuroscience | 46 | 336 | 550 | 39 |
| Computer Science | 142 | 143 | 320 | 33 |
| Engineering and Technology | 175 | 74 | 213 | 34 |
The primary areas of discussion in Journal of Neural Engineering are Artificial intelligence, Brain–computer interface, Neuroscience, Electroencephalography and Pattern recognition. In addition to Artificial intelligence research, it aims to explore topics under Machine learning and Computer vision. The studies in Brain–computer interface featured incorporate elements of Speech recognition, Decoding methods, Task (project management) and Simulation.
Most of the Neuroscience studies addressed also intersect with Deep brain stimulation. Studies on Electroencephalography discussed in it link to the field of Audiology. Journal of Neural Engineering features studies on Pattern recognition, including topics such as Feature extraction.
The research on Stimulation tackled can also make contributions to studies in the areas of Retina, Retinal and Biomedical engineering. Retinal research is the primary subject tackled in Journal of Neural Engineering with a focus on Retinal ganglion. Most of the works presented in Journal of Neural Engineering deals with Biomedical engineering but it intersects with the subject of Microelectrode.
The journal articles primarily tackle Brain–computer interface, Artificial intelligence, Electroencephalography, Neuroscience and Speech recognition. The journal articles address concerns in Brain–computer interface which are intertwined with other disciplines, such as Linear discriminant analysis, Simulation, Human–computer interaction and Signal processing. The most cited papers explore topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning, Computer vision and Pattern recognition.
Journal of Neural Engineering focuses largely on the fields of Artificial intelligence, Brain–computer interface, Pattern recognition, Electroencephalography and Neuroscience. Topics in Artificial intelligence were tackled in line with various other fields like Machine learning and Decoding methods. While work presented in it provided substantial information on Brain–computer interface, it also covered topics in Field (computer science), Speech recognition, Task (project management) and Physical medicine and rehabilitation.
The Pattern recognition works featured in the journal incorporate elements from Signal, Feature (computer vision) and Robustness (computer science). Issues in Electroencephalography were discussed, taking into consideration concepts from other disciplines like Resting state fMRI, Classifier (linguistics), Audiology and Epilepsy. The study on Stimulation presented in Journal of Neural Engineering intersects with the topics under Biomedical engineering.
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 Journal of Neural Engineering (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 Journal of Neural Engineering (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, 1.40% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.34% were posted by at least one author from the top 10 institutions publishing in the journal. Another 13.64% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.74% of all publications and 50.28% 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.
In aspiring to navigate the dynamic and complex field of Neural Engineering, it's crucial to expand your breadth of knowledge and continually develop your skills. To aid your journey, we've gathered a collection of insightful resources that can provide additional perspectives and knowledge.
The integration of Speech recognition in Brain-Computer Interfaces is a rapidly-evolving discipline, intersecting with several areas of Neural Engineering, including Audiology and Pattern recognition. For researchers interested in this particular field, becoming a licensed speech-language pathologist could provide a unique and invaluable perspective. To learn about the path towards this career, consider reviewing the New Mexico SLP license requirements.
It's our firm belief that a holistic education and continuous learning can help stimulate innovation in this budding field. Never hesitate to explore additional educational resources or career pathways - they just might lead to your next research breakthrough.
Ravikiran Mane;Tushar Chouhan;Cuntai Guan
(2020)Xiang Zhang;Lina Yao;Xianzhi Wang;Jessica J M Monaghan
(2021)Hubert J. Banville;Omar Chehab;Aapo Hyvärinen;Denis-Alexander Engemann
(2021)Haoming Zhang;Mingqi Zhao;Mingqi Zhao;Chen Wei;Dante Mantini
(2021)Ce Zhang;Young-Keun Kim;Azim Eskandarian
(2021)Aaron Fleming;Aaron Fleming;Nicole Stafford;Stephanie Huang;Stephanie Huang;Xiaogang Hu;Xiaogang Hu
(2021)Yun Luo;Li-Zhen Zhu;Zi-Yu Wan;Bao-Liang Lu
(2020)Kun Wang;Minpeng Xu;Yijun Wang;Shanshan Zhang
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