Computational Intelligence for Brain Computer Interfaces (IEEE CIBCI)

IEEE SSCI 2020 will run as a virtual conference.

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 cannot 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

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.
• Computational intelligence for emerging BCI applications, e.g., Multimodal and multiparadigm BCI, Hybrid BCI systems, Collaborative BCI, Neuro-robotics, Neurorehabilitation, Passive BCI, Affective BCI, Virtual Reality BCI.
• Online and offline BCI applications, e.g., cognitive-state assessment, human performance enhancement, human-agent teaming, brain robot interface.
• Different modalities of BCIs, e.g., EEG, MEG, fMRI, fNIRS, ECoG, Spikes, LFPs, etc.
• Invasive and non-invasive BCIs.

Symposium Chairs

Program Committee

• Kurtulus Izzetoglu, Drexel University, USA
• Anca Ralescu, University of Cincinnati, USA
• Weping Ding, Nantong University, China
• Dongrui Wu, Huazhong University of Science and Technology, China
• Atulya Nagar, Liverpool Hope University, UK
• Anirban Chowdhury, University of Essex, UK
• David R. Acchanccaray Diaz, Tohoku University, Japan
• Zehong Cao, University of Tasmania, Australia
• Hong Zeng, School of Instrument Science and Engineering Southeast University, China
• Akshansh Gupta, Jawaharlal Nehru University, India
• Christian Flores Vega, University of Technology of Peru, Peru
• Avinash Singh, University of Technology Sydney, Australia
• Jung-Tai King, National Chiao Tung University, Taiwan
• Akshansh Gupta, Jawaharlal Nehru University, India
• Danilo Pelusi, University of Teramo, Italy
• Rifai Chai, Swinburne University of Technology, Australia
• Chun-Shu Wei, National Chiao Tung University, Taiwan