Journal of Neural Engineering

Aims & Scope

The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering.

The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels.

The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.8

Journal Rank

Year	Value
2024	3964

Journal Citation Indicator

Year	Value
2024	4761

SJR (SCImago Journal Rank)

Year	Value
2024	1.127

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	142

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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 Engineering and 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.

• EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

  Citation: 3266

  Authors: Vernon J, Amelia J, Nicholas R, Stephen M, Chou P, Brent J

• A review of classification algorithms for EEG-based brain–computer interfaces

  Citation: 2062

  Authors: F, M, A, F, B

• A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

  Citation: 1651

  Authors: F, L, A, M, M, A, F

• Deep learning for electroencephalogram (EEG) classification tasks: a review

  Citation: 1219

  Authors: Alexander, Yongtian, Jose L

• Deep learning-based electroencephalography analysis: a systematic review

  Citation: 990

  Authors: Yannick, Hubert, Isabela, Alexandre, Tiago H, Jocelyn

• A brain–computer interface using electrocorticographic signals in humans

  Citation: 953

  Authors: Eric C, Gerwin, Jonathan R, Jeffrey G, Daniel W

• An optical neural interface:<i>in vivo</i>control of rodent motor cortex with integrated fiberoptic and optogenetic technology

  Citation: 846

  Authors: Alexander M, Li-Ping, Feng, Leslie A, Murtaza Z, M Bret, Karl

• EEG artifact removal—state-of-the-art and guidelines

  Citation: 752

  Authors: Jose Antonio, Begoña

• A comprehensive review of EEG-based brain–computer interface paradigms

  Citation: 744

  Authors: Reza, Soheil, Eric W, Yang, Xiaopeng

• A novel deep learning approach for classification of EEG motor imagery signals

  Citation: 711

  Authors: Yousef Rezaei, Ugur

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