| Computer Technology, Electronic Communications Technologies |
|
|
|
|
| SSVEP brain-computer interface (BCI) system using smoothed pseudo Wigner-Ville distribution |
| TONG Ji-jun, LI Lin, LIN Qin-guang, ZHU Dan-hua |
1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China;
2. State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases,The First Affiliated Hospital, College of Medicine, Zhejiang University, 310003 Hangzhou, China |
|
|
|
Abstract The multi-frequencies stimulus brain-computer interface (BCI) system based on SSVEP was proposed. Electroencephalography (EEG) is preprocessed and spatial filtered firstly due to its weakness and non-stationary characteristics. The time-frequency analysis method of smoothed pseudo Wigner-Ville distribution was used to extract the maximum frequency components of the EEG signal as the target frequency, while the EEG signal in the length of time window was converted into a time-frequency distribution signal and the visual stimuli EEG frequency time was classified and summarized. As results, the time-frequency analysis method of SPWVD shows advantages with the increase of the length of time windows. When the length of time window reaches 4 s, the classification accuracy of SPWVD reaches 98.29%and the information transmission rate reaches 28.01 bit/min, which is superior to the results by the classic methods of canonical correlation analysis (CCA) and power spectraldensity analysis (PSDA), respectively.
|
|
Published: 01 March 2017
|
|
|
|
Cite this article:
TONG Ji-jun, LI Lin, LIN Qin-guang, ZHU Dan-hua. SSVEP brain-computer interface (BCI) system using smoothed pseudo Wigner-Ville distribution. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 598-604.
|
采用平滑伪Wigner-Ville分布的SSVEP脑机接口系统
提出基于多频率刺激源诱发SSVEP的脑机接口（BCI）系统.针对脑电信号的微弱性和非平稳性特点,在对其进行预处理和空间滤波的基础上,采用平滑伪Wigner-Ville分布的时频分析方法将时间窗口长度内的脑电信号转换为时间-频率分布的信号.分类汇总视觉刺激时间内的脑电频率,并提取脑电信号中的最大频率成分作为目标频率.实验结果表明：随着分析时间窗的增大,平滑伪Wigner-Ville时频分析方法具有一定的优势.当时间窗为4 s时,其分类准确率达98.29%,信息传输率达28.01 bits/min,超过经典的典型相关分析（CCA）和功率谱密度分析(PSDA)的结果.
|
|
| ［1］ WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brain-computer interfaces for communication and control ［J］. Clinical Neurophysiology, 2002, 113(6):767-791.
［2］ WANG Y, GAO X, HONG B, et al. Brain-computer interfaces based on visual evoked potentials ［J］. Engineering in Medicine and Biology Magazine, IEEE, 2008,27(5): 64-71.
［3］ HE B, BAIRD R, BUTERA R, et al. Grand challenges in interfacing engineering with life sciences and medicine ［J］. IEEE Transactions on Biomedical Engineering, 2013, 60(3): 589-598.
［4］ FAN X, BI L, TENG T, et al. A brain-computer interface-based vehicle destination selection system using P300 and SSVEP signals ［J］. Intelligent Transportation Systems Transactions on IEEE, 2015, 16(1): 274-283.
［5］ VIALATTE F, MAURICE M, DAUWELS J, et al. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives ［J］. Progress in Neurobiology, 2010, 90(4): 418-438.
［6］ ZHANG Y, XU P, LIU T, et al. Multiple frequencies sequential coding for SSVEP-based brain-computer interface ［J］. PloSone, 2012, 7(3): e29519.
［7］ YIN E, ZHOU Z, JIANG J, et al. A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm ［J］. Journal of neural engineering, 2013,10(2): 026012.
［8］ XU M, QI H, WAN B, et al. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature ［J］. Journal of neural engineering, 2013,10(2): 026001.
［9］ CHENG M, GAO X, GAO S, et al. Design and implementation of a brain-computer interface with high transfer rates ［J］. IEEE Transactions on Biomedical Engineering, 2002, 49(10): 1181-1186.
[10] FRIMAN O, CEDEFAMN J, LUNDBERG P, et al. Detection of neural activity in functional MRI using canonical correlation analysis \[J\]. Magnetic Resonance in Medicine, 2001, 45(2): 323-330.
［11］ LIN Z, ZHANG C, WU W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs ［J］. IEEE Transactions on Biomedical Engineering, 2006, 53(12): 2610-2614.
［12］ ZHANG Y, ZHOU G, ZHAO Q, et al. Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs ［C］ ∥ 19th International Conference of Neural Information Processing. ICONIP 2012. Doha: Springer Berlin Heidelberg, 2011:287-295.
［13］ TIBSHIRANI R. Regression shrinkage and selection via the lasso ［J］. Journal of the Royal Statistical Society： Series B (Methodological), 1996(6): 267-288.
［14］ 张宇.基于视觉诱发电位的脑—机接口分析算法优化及实时控制系统构建［D］.上海：华东理工大学, 2013.
ZHANG Yu. Analysis algorithm optimization and real-time control system establishment for brain-computer interface based on visual evoked potentials ［D］. Shanghai: East China University of Science and Technology, 2013.
［15］ FRIMAN O, VOLOSYAK I, GRSER A. Multiple channel detection of steadystate visual evoked potentials for braincomputer interfaces ［J］. IEEE Transactions on Biomedical Engineering， 2007, 54(4): 742-750.
［16］ 何庆华,彭承琳,吴宝明.脑机接口技术研究方法［J］.重庆大学学报:自然科学版,2002, 25(12): 106-109.
HE Qing-hua, PENG Cheng-lin, WU Bao-ming. Research methods of brain-computer interface technology ［J］. Journal of Chongqing University: Natural ScienceEdition, 2002,25(12): 106-109.
［17］ GARCIAMOLINA G, ZHU D. Optimal spatial filtering for the steady state visual evoked potential: BCI application ［C］ ∥ 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER). Cancun: IEEE. 2011: 156-160.
［18］ MLLER-PUTZ G R, SCHERER R, BRAUNEIS C, et al. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components ［J］. Journal of Neural Engineering, 2005,2(4): 123.
［19］ 张杨松.基于稳态视觉诱发电位的脑机制及脑机接口研究［D］.成都：电子科技大学,2013.
ZHANGYang-song. Research on steady state visualevoked potential of its brain mechanisms and application in brain-computer interface ［D］. Chengdu: University of Electronic Science and Technology of China,2013.
［20］ HART E P, DUMAS E M, REIJNTJES R, et al. Deficient sustained attention to response task and P300 characteristics in early Huntington’s disease ［J］. Journal of Neurology, 2012, 259(6): 1191-1198. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
