Aim and Scope
Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Recently, many advanced machine-learning technologies have appeared, such as deep learning, transfer learning, and so on. Deep learning has achieved great success in image and video analysis, natural language processing, speech recognition, etc., and recently has also started to find applications in BMI/BCI. Transfer learning makes use of data or knowledge gained in solving one problem to help solve a different, albeit related, problem. Transfer learning can be particularly useful in BMI/BCI to cope with variability across individuals or tasks, accelerating learning and improving performance. Deep learning and transfer learning can also be integrated to take advantage of both domains.
Although the studies of brain-machine interfaces using the advanced machine-learning methods become more and more popular, there are many fundamental problems unsolved so far, such as deep-learning representation of some EEG-based BMI/BCI data from multiple modalities, mapping data from one modality to another to achieve cross-source BMI/BCI data analysis, identifying and utilizing relations between elements from two or more different signal sources for comprehensive BMI/BCI data analysis, fusing information from two or more signal sources to perform a more accurate prediction, transferring knowledge between modalities and their representations, and recovering missing modality data given the observed ones. In the past decade, several EEG-based BMI/BCI methods and technologies have been developed and shown promising results in some real-world examples such as neuroscience, medicine, and rehabilitation, which led to a proliferation of papers showing accuracy/performance and comparison, but most do not advance to real-time translation or application. For all the reasons mentioned above, it inspires us to exploit and develop effective advanced machine-learning algorithms for addressing fundamental issues in the BMI/BCI field.
This special issue aims at providing a forum for researchers from BMI/BCI and machine learning to present recent progress in machine-learning research with applications to BMI/BCI data. These papers should contain the innovation of theory, a detailed experimental analysis, and some related clinical translations. The list of possible topics includes, but is not limited to:
- Machine-learning algorithms with applications
- Convolutional/recurrent neural networks for BMI/BCI, deep feed forward/belief/residual networks for BMI/BCI, extreme learning machines for BMI/BCI, generative adversarial networks for BMI/BCI, long short-term memory for BMI/BCI, transfer learning for BMI/BCI, domain adaptation for BMI/BCI, covariate shift for BMI/BCI, deep-transfer learning for BMI/BCI, BMI/BCI-based healthcare systems, advanced machine-learning technology for BMI/BCI, and highly interpretable fuzzy systems for BMI/BCI
- Technological advances, including real-time systems for BMI/BCI and clinical translation including rehabilitation and assistive technologies, neuroprosthesis, augmented and virtual reality embodiments, and pathways from the brain to the external world via mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions
- An in-depth survey on the BMI/BCI field and its application by a highly qualified expert
Important Dates
- Submission deadline: CLOSED
- Notification of the first-round review: June 30, 2020
- Revised submission due: August 31, 2020
- Final notice of acceptance/rejection: October 31, 2020
Submission Guidelines
Prospective authors are invited to submit their manuscripts electronically after the “open for submissions” date, adhering to the IEEE/ACM Transactions on Computational Biology and Bioinformatics guidelines (https://www.computer.org/csdl/journal/tb/write-for-us/15053). Please submit your papers through the online system (https://mc.manuscriptcentral.com/tcbb-cs) and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by email to the guest editors directly.
Guest Editors
(1) Dr. Kaijian Xia. China University of Mining Technology/ The affiliated Changshu Hospital of Soochow University, China. lb17060008@cumt.edu.cn
(2) Dr. Yizhang Jiang. Jiangnan University, China. yzjiang@jiangnan.edu.cn
(3) Prof. Yudong Zhang. University of Leicester, UK. yudongzhang@ieee.org, yudong.zhang@le.ac.uk
(4) Dr. Wen Si. University of South Florida, USA. wensi@mail.usf.edu
