Design of SSVEP Enhancement-Based Brain Computer Interface

作     者：Lin, Bor-Shing Wang, Hsiao-An Huang, Yao-Kuang Wang, Yu-Lin Lin, Bor-Shyh 

作者机构：Natl Taipei Univ Dept Comp Sci & Informat Engn New Taipei 23741 Taiwan Natl Chiao Tung Univ Inst Imaging & Biomed Photon Tainan 71150 Taiwan Chiayi Chang Gung Mem Hosp Div Cardiovasc Surg Puzi City 61363 Chiayi County Taiwan Chi Mei Med Ctr Dept Rehabil Tainan 71004 Taiwan 

出 版 物：《IEEE SENSORS JOURNAL》 (IEEE传感器杂志)

年 卷 期：2021年第21卷第13期

页      面：14330-14338页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0702[理学-物理学] 

基　　金：Ministry of Science and Technology in Taiwan [MOST 108-2221-E-009-054-MY2, MOST 109-2314-B-305-001, MOST 109-2221-E-305-001-MY2] Higher Education Sprout Project of National Chiao Tung University Ministry of Education (MOE), Taiwan University System of Taipei Joint Research Program [USTP-NTPU-TMU-109-03] Faculty Group Research Funding Sponsorship by National Taipei University [2020-NTPU-ORDA-02] 

主　　题：Electroencephalography Electrodes Pins Metals Skin Brain-computer interfaces Visualization Brain computer interface steady state visually evoked potentials field programmable gate array SSVEP enhancement 

摘      要：Brain computer interface (BCI) system is a communication bridge between the brain and the external device, and it has been rapidly developed in the recent years. Here, steady state visually evoked potential (SSVEP) is one of the most frequently used control methods for BCI due to its advantages of low training requirement and high stability. However, some people present unobvious SSVEP feature at the location of the primary visual cortex, and this will reduce the performance of SSVEP-based BCIs. In this study, a novel field programmable gate array (FPGA)-based brain computer interface with SSVEP enhancement is proposed to improve the above issue. In the proposed system, a SSVEP-enhancement active dry electrode is designed to acquire good quality of electroencephalography (EEG) without conductive gels, and further enhance the local EEG signal. The experimental results show the proposed system can effectively improve the signal-to-noise ratio of SSVEP and the information transfer rate. Moreover, compared with the current SSVEP-based BCIs in the previous studies, the proposed BCI system contains the advantages of local EEG enhancement, wearablility, wireless transmission, front-end BCI translation, and it contains the potential of applying in many BCI applications in daily life.