2019 IEEE Symposium Series on Computational Intelligence

IEEE Symposium Series on Computational Intelligence

December 6-9, 2019 Xiamen, China


Computational Intelligence for brain computer interfaces (CIBCI)

A brain-computer interface (BCI) is a communication pathway for a user to interact with his/her surroundings by using brain signals, which contain information about the user's cognitive state or intentions. The brain signals could be non-invasive, e.g., the scalp electroencephalogram (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS), and invasive, e.g., electrocorticography (ECoG). Early BCI systems were mainly used to help people with disabilities. For example, motor imagery based BCIs have been used to help severely paralyzed patients to control powered exoskeletons or wheelchairs without the involvement of muscles, and event related potential spellers enable patients who can not move nor speak to type. Recently, the application scope of BCIs has been extended to able-bodied people.
However, there are still many challenges in the transition of BCIs from laboratory settings to real-life applications, including the reliability and convenience of the sensing hardware, and the availability of high-performance and robust algorithms for signal analysis and interpretation. This symposium focuses on the latter. It will discuss how advances in computational intelligence can facilitate BCI signal processing, feature extraction, and pattern recognition, in order to make them more robustness and reliability in everyday applications.


Topics of interest include, but are not limited to:

  • Computational intelligence for BCI signal processing, e.g., ICA, CSP, CCA, etc.
  • Computational intelligence for BCI feature extraction, e.g., time-domain, frequency domain, time-frequency domain, spatiotemporal features, Riemannian Geometry, etc.
  • Computational intelligence for BCI pattern recognition, e.g., deep learning, transfer learning, ensemble learning, reinforcement learning, active learning, multi-view learning, etc.
  • Invasive and non-invasive BCIs.
  • Online and offline BCI applications.
  • Different modalities of BCIs, e.g., EEG, MEG, fMRI, fNIRS, ECoG, Spikes, LFPs, etc.

Symposium Chairs

Dongrui Wu

Huazhong University of Science and Technology, Wuhan, Hubei, China

Email: drwu@hust.edu.cn


Hong Zeng

School of Instrument Science and Engineering Southeast University, Nanjing, Jiangsu, China

Email: hzeng@seu.edu.cn


Zhigang Zeng

School of Automation Huazhong University of Science and Technology, Wuhan, Hubei, China

Email: zgzeng@hust.edu.cn


Program Committee

Ricardo Chavarriaga ricardo.chavarriaga@epfl.ch École polytechnique fédérale de Lausanne (EPFL), Switzerland
Yufei Huang Yufei.Huang@utsa.edu University of Texas, San Antonio
Chin-Teng Lin chintenglin@gmail.com University of Technology, Sydney
Tzyy-Ping Jung tpjung@ucsd.edu University of California, San Diego
Faiyaz Doctor aa9536@coventry.ac.uk Coventry University, UK
Christian Wagner Christian.Wagner@nottingham.ac.uk University of Nottingham, UK
Yizhang Jiang jyz0512@163.com Jiangnan University, China
Hong Zeng hzeng@seu.edu.cn Southeast University, China
Dongrui Wu drwu@hust.edu.cn Huazhong University of Science and Technology, China