Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2017 , Vol. 51 >Issue 12: 1456 - 1463

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2017.12.008

The Brain-Computer Interface Using Functional Near-Infrared Spectroscopy

Expand
  • National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, China

Online published: 2017-11-30

Fold

Abstract

This paper explored the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion, as well as the affection of prefrontal cortex on the classification accuracy of MI and ME. We measured changes of oxygenated hemoglobin HbO2 and deoxygenated hemoglobinon (Hb) on prefrontal cortex (PFC) and motor cortex (MC) when 15 subjects performed hand extension and finger tapping tasks. Then mean, slope, quadratic coefficient and approximate entropy features were extracted from HbO2 as the input of support vector machine. With the four-class classifiers of brain-computer interface using functional near-infrared (fNIRS-BCI spectroscopy), 87.65% and 87.58% classification accuracy were realized corresponding to hand extension and finger tapping tasks. The classification accuracy increased significantly after adding PFC fNIRS signal, and greater increase emerged in finger napping than hand extension. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion, and the PFC region is sensitive in the four-class fNIRS-BCI classifiers.

Key words： functional near-infrared spectroscopy(fNIRS); brain-computer interface(BCI); motor imagery(MI); motor execution (ME); support vector machine (SVM)

Cite this article

JIAO Xuejun,ZHANG Zhen,JIANG Jin,WANG Chunhui,YANG Hanjun,XU Fenggang,CAO Yong,FU Jiahao . The Brain-Computer Interface Using Functional Near-Infrared Spectroscopy[J]. Journal of Shanghai Jiaotong University, 2017 , 51(12) : 1456 -1463 . DOI: 10.16183/j.cnki.jsjtu.2017.12.008

References

［1］ASENSIO-CUBERO J, GAN J Q, PALANIAPPAN R. Multiresolution analysis over graphs for a motor imagery based online BCI game［J］. Computers in Biology & Medicine, 2016, 68: 21-26. ［2］XU M, QI H, WAN B, et al. A hybrid BCI speller paradigm combining P300 potential and SSVEP blocking feature［J］. Journal of Neural Engineering, 2013, 10(2): 26001-26013. ［3］LOTTE F, FALLER J, GUGER C, et al. Combining BCI with virtual reality: Towards new applications and improved BCI［M］. Berlin: Springer Berlin Heidelberg, 2012: 197-220. ［4］TUMANOV K, GOEBEL R, MCKEL R, et al. fNIRS-based BCI for robot control［C］∥Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. Istanbul: International Foundation for Autonomous Agents and Multiagent Systems, 2015: 1953-1954. ［5］PICHIORRI F, MORONE G, PETTI M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery［J］. Annals of Neurology, 2015, 77(5): 851-865. ［6］BAMDAD M, ZARSHENAS H, AUAIS M A. Application of BCI systems in neurorehabilitation: A scoping review［J］. Disability & Rehabilitation Assistive Technology, 2015, 10(5): 1-10. ［7］GAUME A, ABBASI M A, DREYFUS G, et al. Towards cognitive BCI: Neural correlates of sustained attention in a continuous performance task［C］∥2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). Montpellier: IEEE, 2015: 1052-1055. ［8］HAJIPOUR S S, SHAMSOLLAHI M B. Selection of efficient features for discrimination of hand movements from MEG using a BCI competition IV data set［J］. Frontiers in Neuroscience, 2012, 6(6): 42. ［9］ZICH C, DEBENER S, KRANCZIOCH C, et al. Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery［J］. NeuroImage, 2015, 114: 438-447. ［10］POWER S D, FALK T H, CHAU T. Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy［J］. Journal of Neural Engineering, 2010, 7(2): 1393-1402. ［11］NASEER N, HONG K S. Functional near-infrared spectroscopy based discrimination of mental counting and no-control state for development of a brain-computer interface［C］∥2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE, 2013: 1780-1783. ［12］CHAN A, EARLY C E, SUBEDI S, et al. Systematic analysis of machine learning algorithms on EEG data for brain state intelligence［C］∥Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. Washington: IEEE, 2015: 793-799. ［13］HORTAL E,BEDA A, IEZ E, et al. Online classification of two mental tasks using a SVM-based BCI system［C］∥Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on. San Diego: IEEE, 2013: 1307-1310. ［14］HENNRICH J, HERFF C, HEGER D, et al. Investigating deep learning for fNIRS based BCI［C］∥2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milano: IEEE, 2015: 2844-2847. ［15］CHAE Y, JEONG J, JO S. Toward brain-actuated humanoid robots: Asynchronous direct control using an EEG-based BCI［J］. IEEE Transactions on Robotics, 2012, 28(5): 1131-1144. ［16］MIN H C, LEE J S, HEO J, et al. Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI［J］. Journal of Neuroscience Methods, 2016, 258: 104-113. ［17］KHAN M J, HONG K S. Passive BCI based on drowsiness detection: An fNIRS study［J］. Biomedical Optics Express, 2015, 6(10): 4063-4078. ［18］POWER S D, KUSHKI A, CHAU T. Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: Toward a three-state NIRS-BCI［J］. Bmc Research Notes, 2012, 5(1): 1-10. ［19］AGUILAR J M, CASTILLO J, ELIAS D. EEG signals processing based on fractal dimension features and classified by neural network and support vector machine in motor imagery for a BCI［C］∥VI Latin American Congress on Biomedical Engineering CLAIB 2014. Paraná：Springer International Publishing, 2015: 615-618. ［20］ZIMMERMANN R, MARCHAL-CRESPO L, EDELMANN J, et al. Detection of motor execution using a hybrid fNIRS-biosignal BCI: A feasibility study［J］. Journal of Neuroengineering & Rehabilitation, 2013, 10(1): 1-15. ［21］NASEER N, HONG K S. Classification of functional near-infrared spectroscopy signals corresponding to right-and left-wrist motor imagery for development of a brain-computer interface.［J］. Neuroscience Letters, 2013, 553(8): 84-89. ［22］KAISER V, BAUERNFEIND G, KREILINGER A, et al. Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG［J］. Neuroimage, 2014, 85(1): 432-444. ［23］SUZUKI M, MIYAI I, ONO T, et al. Activities in the frontal cortex and gait performance are modulated by preparation. An fNIRS study［J］. Neuroimage, 2008, 39(2): 600-607. ［24］HARADA T, MIYAI I, SUZUKI M, et al. Gait capacity affects cortical activation patterns related to speed control in the elderly［J］. Experimental Brain Research, 2008, 193(3): 445-454. ［25］OBRIG H, HIRTH C, JUNGE-HLSING J G, et al. Cerebral oxygenation changes in response to motor stimulation.［J］. Journal of Applied Physiology, 1996, 81(3): 1174-1183. ［26］TARKKA I M, STOKIC D S. Left prefrontal cortex contributes to motor imagery: A pilot study［J］. Research in Neuroscience, 2013, 2(2): 19-23. ［27］KANTHACK T F D, BIGLIASSI M, ALTIMARI L R. Equal prefrontal cortex activation between males and females in a motor tasks and different visual imagery perspectives: A functional near-infrared spectroscopy (fNIRS) study［J］. Motriz Revista De Educao Física, 2013, 19(3): 627-632. ［28］WRIESSNEGGER S C, KURZMANN J, NEUPER C. Spatio-temporal differences in brain oxygenation between movement execution and imagery: A multichannel near-infrared spectroscopy study［J］. International Journal of Psychophysiology, 2008, 67(1): 54-63. ［29］JANG K E, TAK S, JUNG J, et al. Wavelet minimum description length detrending for near-infrared spectroscopy.［J］. Journal of Biomedical Optics, 2009, 14(3): 659-660. ［30］NISHIYORI R, BISCONTI S, ULRICH B. Motor cortex activity during functional motor skills: An fNIRS study［J］. Brain Topography, 2015, 29(1): 1-14. ［31］KIRILINA E， JELLOW A，HEINE A, et al. The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy［J］. NeuroImage, 2012, 61(1): 70-81. ［32］CUI X, BRAY S, REISS A L. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics［J］. Neuroimage, 2010, 49(4): 3039-3046. ［33］JIAO X, BAI J, CHEN S, et al. Research on mental fatigue based on entropy changes in space environment［C］∥2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings. Tianjin: IEEE, 2012: 74-77. ［34］BENARON D A, HINTZ S R, VILLRINGER A, et al. Noninvasive functional imaging of human brain using light［J］. Journal of Cerebral Blood Flow & Metabolism Official Journal of the International Society of Cerebral Blood Flow & Metabolism, 2000, 20(3): 469-477. ［35］HONG K S, NASEER N, KIM Y H. Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI［J］. Neuroscience Letters, 2014, 587: 87-92.
Options
Outlines

/

〈 〉