Aims & Scope

Aims:

Brain-computer interface (BCI), also known as Brain Machine Interface (BMI), is an exponentially growing multidisciplinary field of research that primary aim is to establish a direct communication pathway between a human or animal brain and external devices, like a computer or robots, bypassing traditional neural pathways.

The journal Neural Engineering and Neurofeedback (NEN) aims to publish high-quality reviews, tutorials, and original research papers on the theoretical and practical advances in BCI and neurofeedback technologies and related topics. It mostly focuses on improving, understanding, and implementing BCI and neurofeedback technologies for various applications, including rehabilitation, medical applications, communication, and cognitive enhancement.

The editorial policy of the journal is to publish high-quality research, review and tutorial papers, which contain substantial contributions and/or validations/evaluation of performance, supported by computer simulations or comparisons. Mathematically and AI/Machine learning-oriented papers on BCI and related topics of exceptional interest to the BCI community will also be considered. High priority will be given to contributions concerned with a discussion of the state of the arts BCIs, establishment of appropriate innovative models, analytical or numerical, and a discussion of the relevance of the results when applied in practical potential BCI applications.

Our policy and philosophy is that all research is for the benefit of humankind and research results should be returned to all people without borders or discrimination, serving humanity worldwide and in a transparent fashion.

Scope: Papers are solicited from but not limited to the following topics:

• Noninvasive BCI, based on Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Near-Infrared Spectroscopy (fNIRS), Transcranial Magnetic Stimulation (TMS), and other technologies.
• Invasive BCI, based on electrocorticography (ECoG), stereotactic electroencephalography (sEEG) (also known as Stereo-SEEG), intracranial electroencephalography (iEEG), and other emerging technologies.
• Hybrid BCI systems that combine both invasive and non-invasive techniques.
• Affective BCI, especially detection, recognition, and classification of human or animal emotions.
• Speech BCI that translates brain activity into text or synthesized speech, e.g., enabling communication for individuals who have lost the ability to speak. 
• BCI based on AI, especially based on deep neural networks and advanced machine learning.
• BCI and neurofeedback (NF) used for sensory feedback, e.g., visual, auditory and/or tactile feedbacks.
• BCI and neurofeedback based on advanced techniques like EEG, MEG, ECoG, fMRI, and PET to understand brain activity and cognitive functions.
• Applications of neurofeedback to enhance performance in areas like athletics and academics and to treat various mental health conditions, like insomnia, pain, learning disabilities, anxiety, depression, ADHD, and PTSD.
• FMRI-BCI systems employing real-time fMRI technology that enables various applications, including training to self-regulate activity, plasticity, and functional reorganization.
• Therapeutic BCI systems for therapy, neurorehabilitation, BCIs used to enhance the cognitive and physical capabilities of humans, such as memory, learning, and attention.
• Hardware and software for BCI and neurofeedback, especially development of new electrodes, sensors, stimulus, and data acquisition and visualization.
• Signal processing, machine learning, and mathematical methods for extraction and selection of temporal, spectral, and spatial features of brain signals for clustering, classification, and recognition of brain patterns.
• Fusion and processing brain data and behavior data for humans and animals.
• BCI applications for education and training, health monitoring, gaming, entertainment, sports, and environmental control.
• Controlling various assistive devices via BCI, e.g., wheelchair, robotic arms, or a neuro-prosthesis.
• Comparison of the performance of various BCI systems.
• Brain-inspired technologies, e.g., robots and autonomous systems that use brain-inspired algorithms for navigation and environmental perception. 
• Neuromorphic computing and chips designed to mimic the brain's architecture and brain information processing, e.g., leading to more energy-efficient and powerful computing.
• BCI challenges, especially ethical concerns, reliability, robustness, and accessibility.