DOI: 10.14704/nq.2018.16.3.1200

Design and Implementation of Real-time Brain-computer Interface System Based on LabVIEW

Chang Liu

Abstract


Brain-computer interface (BCI) is a newly developed man-machine interface method that is applied in the field of brain science, rehabilitation engineering, biomedical engineering and human-machine automatic control. Under the LabVIEW environment of virtual instrument graphical programming language, this paper improves the program of Active One, a physiological signal acquisition system. The off-line processing system of electroencephalogram(EEG) is improved to a real-time processing system and a real-time feedback BCI experiment system is established based on visual evoked potential (VEP). The cumulative average method combined with the FIR filter method was used to extract the VEP signal and form a closed-loop BCI. Real-time feedback of the BCI system was realized and the VEP signal was automatically identified. Using the proposed method of this paper to implement BCI can achieve a higher accuracy with certain practicality and feasibility.

Keywords


Brain-computer interface (BCI), Visual evoked potentials (VEP), LabVIEW, Signal Processing

Full Text:

PDF

References


Andruseac GG, Paturca SV, Banica CK, Costea IM, Rotariu C. A novel method of teaching information technology applied in health monitoring. Journal of Biotechnology 2016; 239: 1-3.

Chouder A, Silvestre S, Taghezouit B, Karatepe E. Monitoring, modelling and simulation of pv systems using labview. Solar Energy 2013; 91(3): 337-49.

Clark VP, Hillyard SA. Spatial selective attention affects early extrastriate but not striate components of the visual evoked potential. Journal of Cognitive Neuroscience 1996; 8(5): 387-402.

Di Russo F, Martínez A, Sereno MI, Pitzalis S, Hillyard SA. Cortical sources of the early components of the visual evoked potential. Human Brain Mapping 2002; 15(2): 95-111.

Dierick M, Masschaele B, Van Hoorebeke L. Octopus, a fast and user-friendly tomographic reconstruction package developed in labview. Measurement Science & Technology 2004; 15(7): 1366-1370.

Faraco G, Gabriele L. Using labview for applying mathematical models in representing phenomena. Computers & Education 2007; 49(3): 856-72.

Jansen BH, Rit VG. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics 1995; 73(4): 357-66.

Kotchetkov IS, Hwang BY, Appelboom G, Kellner CP, Jr CE. Brain-computer interfaces: military, neurosurgical, and ethical perspective. Neurosurgical Focus 2010; 28(5): E25: 1-6.

Kübler A. Brain-computer interfacing: science fiction has come true. Brain 2013; 136(6): 2001-04.

Kullmann PH, Wheeler DW, Beacom J, Horn JP. Implementation of a fast 16-bit dynamic clamp using LabVIEW-RT. Journal of Neurophysiology 2004; 91(1): 542-54.

Mohy-Ud-Din Z, Sang HW, Lee JH, Lee MG, Kim JH, Jin HC. Pleasure detection by online brain–computer interface. Measurement 2011; 44(1): 121-28.

Pfurtscheller G, Mllerputz GR, Scherer R, Neuper C. Rehabilitation with brain-computer interface systems. Computer 2008; 41(10): 58-65.

Qiu ZC, Zhang WM, Guo Y, Qin F, Yue MM. Effect of external field on the variation of magnetic memory signals, Mathematical Modelling of Engineering Problems 2014; 1(1): 1-4.

Reilly RB, Reilly RB, Leeb R, Hirose M, Slater M. Brain-computer interfaces, virtual reality, and videogames. Computer 2008; 41(10): 66-72.

Salehi D, Brandt M. Melt pool temperature control using LabVIEW in Nd: YAG laser blown powder cladding process. International Journal of Advanced Manufacturing Technology 2006; 29(3-4): 273-78.

Zhong M, Lotte F, Girolami M, Lécuyer A. Classifying EEG for brain computer interfaces using gaussian processes. Pattern Recognition Letters 2008; 29(3): 354-59.


Supporting Agencies

The work was supported by projects of Heilongjiang Bayi Agricultural University “XDB2014-18” and “NDJY15Z13”.



| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2018