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Advanced Learning Algorithms for Brain-Computer Interfaces

Brain-Computer Interfaces

Interested in contributing a paper?

Brain-computer interface (BCI) is an advanced communication technique that enables the human brain to directly interact with the surrounding environment and external devices such as a computer. Brain activity patterns associated with mental tasks could be decoded based on neurophysiological signals (e.g., EEG) by a BCI system, which translates brain activity patterns representing human thoughts or intentions into visualized information or a command for the purposes of communication and control. For instance, paralyzed people can regain their abilities to interact with the outside world by the use of a BCI system.

Although BCI techniques have been improving and have brought diverse benefits to people, there remains an undoubted need to further enhance the usability and robustness of BCI systems. Due to the low signal-to-noise ratio of neurophysiological signals, perfectly decoding signals is challenging. Advanced machine learning algorithms show promise to improve BCI performance by optimizing signal processing and utilizing sophisticated methods. Recently, artificial intelligence technology has been rapidly developed. It is expected that the practical progress of BCI will get huge benefits from the latest learning algorithms, including robust spatial filtering, advanced signal processing, subspace learning, sparse representation, multi-dimensional optimization, transfer and online learning, Bayesian probabilistic modeling, and deep learning.

You are cordially invited to make a contribution to a special issue of Brain-Computer Interfaces (http://www.tandfonline.com/tbci), entitled “Advanced Learning Algorithms for Brain-Computer Interfaces.” The special issue aims to archive high-quality original research articles as well as review articles on a wide range of topics from theory to applications related to but not limited to:

  • Robust spatial filtering algorithms for EEG pattern optimization
  • Subspace learning for dimensionality reduction of brain signals
  • Sparse representation for EEG feature selection in BCI
  • Multi-dimensional feature learning for BCI
  • Transfer learning for generic modeling of BCI
  • Adaptive machine learning for online model optimization in BCI
  • Probabilistic and Bayesian machine learning methods
  • Deep learning algorithms for BCI applications
  • Advanced signal processing algorithms for artifact mitigation

INSTRUCTIONS FOR AUTHORS

  • The deadline for receipt of papers is 30th May 2019, with a projected publication date of December 2019.  All papers will be subject to the standard peer-review procedures of the journal.

Only original manuscripts will be considered for publication in this special issue of Brain-Computer Interfaces.

 

Special Issue Guest Editors

Yu Zhang,
Stanford University, USA,
yzhangsu@stanford.edu

Toshihisa Tanaka,
Tokyo University of Agriculture and Technology, Japan,
tanakat@cc.tuat.ac.jp

Wei Wu,
Stanford University, USA,
wwumed@stanford.edu

Junhua Li,
National University of Singapore,
Singapore and University of Essex, UK,
juhalee@nus.edu.sg