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A brain-computer interface (BCI) system can recognize the mental activities pattern by computer algorithms to control the external devices. Electroencephalogram (EEG) is one of the most common used approach for BCI due to the convenience and non-invasive implement. Therefore, more and more BCIs have been designed for the disabled people that suffer from stroke or spinal cord injury to help them for rehabilitation and life. We introduce the common BCI paradigms, the signal processing, and feature extraction methods. Then, we survey the different combined modes of hybrids BCIs and review the design of the synchronous/asynchronous BCIs. Finally, the shared control methods are discussed.


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A review of EEG-based brain-computer interface systems design

Show Author's information Wenchang Zhang1,2( )Chuanqi Tan2Fuchun Sun2Hang Wu1Bo Zhang2
Institute of Medical Support Technology, Academy of Military Sciences, Tianjin 300161, China
State Key Lab. of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, the Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Abstract

A brain-computer interface (BCI) system can recognize the mental activities pattern by computer algorithms to control the external devices. Electroencephalogram (EEG) is one of the most common used approach for BCI due to the convenience and non-invasive implement. Therefore, more and more BCIs have been designed for the disabled people that suffer from stroke or spinal cord injury to help them for rehabilitation and life. We introduce the common BCI paradigms, the signal processing, and feature extraction methods. Then, we survey the different combined modes of hybrids BCIs and review the design of the synchronous/asynchronous BCIs. Finally, the shared control methods are discussed.

Keywords:

BCI, EEG, MI, SSVEP, P300
Received: 18 November 2018 Revised: 20 December 2018 Accepted: 31 December 2018 Published: 02 April 2019 Issue date: December 2018
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Publication history

Received: 18 November 2018
Revised: 20 December 2018
Accepted: 31 December 2018
Published: 02 April 2019
Issue date: December 2018

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© The authors 2018

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