Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface

Neuroscience Letters

Volumn 553, Issue , 2013, Pages 84-89

  Naseer, Noman ^a   Hong, Keum Shik ^a  

^a Pusan National University   (South Korea)

Author keywords

Brain computer interface (BCI); Functional near infrared spectroscopy (fNIRS); Wrist motor imagery

Indexed keywords

HEMOGLOBIN;

ACCURACY; ADULT; ARTICLE; BRAIN COMPUTER INTERFACE; CLASSIFICATION; FEASIBILITY STUDY; HAND MOVEMENT; HUMAN; HUMAN EXPERIMENT; IMAGE PROCESSING; IMAGERY; MALE; NEAR INFRARED SPECTROSCOPY; NORMAL HUMAN; PRIMARY MOTOR CORTEX; PRIORITY JOURNAL; SIGNAL PROCESSING; TIME; WRIST;

BRAIN-COMPUTER INTERFACE (BCI); FUNCTIONAL NEAR-INFRARED SPECTROSCOPY (FNIRS); WRIST MOTOR IMAGERY;

ADULT; BRAIN-COMPUTER INTERFACES; DISCRIMINANT ANALYSIS; FUNCTIONAL LATERALITY; HUMANS; IMAGINATION; MALE; MOVEMENT; SPECTROSCOPY, NEAR-INFRARED; TIME FACTORS; WRIST; YOUNG ADULT;

EID: 84884379869     PISSN: 03043940     EISSN: 18727972     Source Type: Journal    
DOI: 10.1016/j.neulet.2013.08.021     Document Type: Article

References (36)

1. Aqil M., Hong K.-S., Jeong M.-Y., Ge S.S. Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity. NeuroImage 2012, 63:553-568.
2. Aqil M., Hong K.-S., Jeong M.-Y., Ge S.S. Cortical brain imaging by adaptive filtering of NIRS signals. Neurosci. Lett. 2012, 514:35-41.
3. Beisteiner R., Hollinger P., Lindinger G., Lang W., Berthoz A. Mental representations of movements: brain potentials associated with imagination of hand movements. Electroencephalogr. Clin. Neurophysiol. 1995, 96:183-193.
4. Benaron D.A., Hintz S.R., Villringer A., Boas D.A., Kleinschmidt A., Frahm J., Hirth C., Obrig H., van Houten J.C., Kermit E.L., Cheong W.F., Stevenson D.K. Noninvasive functional imaging of human brain using light. J. Cereb. Blood Flow Metab. 2000, 20:469-477.
5. Coyle S.M., Ward T.E., Markham C.M., McDarby G. On the suitability of near-infrared (NIR) systems for next generation brain-computer interfaces. Physiol. Meas. 2004, 25:815-822.
6. Coyle S.M., Ward T.E., Markham C.M. Brain-computer interface using a simplified functional near-infrared spectroscopy system. J. Neural Eng. 2007, 4:219-226.
7. Frederick B., Nickerson L.D., Tong Y. Physiological denoising of BOLD fMRI data using regressor interpolation at progressive time delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS). NeuroImage 2012, 60:1913-1923.
8. Gagnon L., Yucel M.A., Dehaes M., Cooper R.J., Perdue K.L., Selb J., Huppert T.J., Hoge R.D., Boas D.A. Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements. NeuroImage 2012, 59:3933-3940.
9. Gratton G., Brumback C.R., Gordon B.A., Pearson M.A., Low K.A., Fabiani M. Effects of measurement method, wavelength, and source-detector distance on the fast optical signal. NeuroImage 2006, 32:1576-1590.
10. Hochberg L.R., Bacher D., Jarosiewicz B., Masse N.Y., Simeral J.D., Vogel J., Haddadin S., Liu J., Cash S.S., van der Smagt P., Donoghue J.P. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 2012, 485:372-375.
11. Hoshi Y. Functional near-infrared spectroscopy: current status and future prospects. J. Biomed. Opt. 2007, 12:062106.
12. Hu X.-S., Hong K.-S., Ge S.S., Jeong M.-Y. Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy. Biomed. Eng. Online 2010, 9:1-15.
13. Hu X.-S., Hong K.-S., Ge S.S. Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series. Neurosci. Lett. 2011, 504:115-120.
14. Hu X.-S., Hong K.-S., Ge S.S. fNIRS-based online deception decoding. J. Neural Eng. 2012, 9:026012.
15. Hu X.-S., Hong K.-S., Ge S.S. Reduction of trial-to-trial variations in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity. J. Biomed. Opt. 2013, 18:017003.
16. Jobsis F.F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 1977, 198:1264-1267.
17. Kamran M.A., Hong K.-S. Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study. J. Neural Eng. 2013, 10:056002.
18. Lotte F., Congedo M., Lecuyer A., Lamarche F., Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 2007, 4:R1-R13.
19. McCormick P.W., Stewart M., Lewis G., Dujovny M., Ausman J.I. Intracerebral penetration of infrared light: technical note. J. Neurosurg. 1992, 76:315-318.
20. Moghimi S., Khushki A., Power S.D., Guerguerian A.M., Chau T. Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy. J. Neural Eng. 2012, 9:026022.
21. Naito M., Michioka Y., Ozawa K., Ito Y., Kiguchi M., Kanazaw T. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Trans. Inform. Syst. 2007, 90:1028-1037.
22. Okada E., Firbank M., Schweiger M., Arridge S.R., Cope M., Delpy D.T. Theortical and experimental investigation of near-infrared light propagation in a model of the adult head. Appl. Opt. 1997, 36:21-31.
23. Pfurtscheller G., Neuper C., Schlogl A., Lugger K. Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehabil. Eng. 1998, 6:316-325.
24. Pfurtscheller G., Neuper C., Guger C., Harkam W., Ramoser H., Schlogl A., Obermaier B., Pregenzer M. Current trends in Graz brain-computer interface (BCI) research. IEEE Trans. Rehabil. Eng. 2000, 8:216-219.
25. Porro C.A., Francescato M.P., Cettolo V., Diamond M.E., Baraldi P., Zuiani C., Bazzocchi M., di Prampero P.E. Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study. J. Neurosci. 1996, 16:7688-7698.
26. Power S.D., Khushki A., Chau T. Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state. J. Neural Eng. 2011, 8:066004.
27. Power S.D., Khushki A., Chau T. Automatic single trial discrimination of mental arithmetic, mental singing and the no-control state from the prefrontal activity: towards a three-state NIRS-BCI. BMC Res. Notes 2012, 5:141.
28. Power S.D., Kushki A., Chau T. Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS. PLoS ONE 2012, 7:e37791.
29. Salvaris M., Sepulveda F. Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface. J. Neural Eng. 2010, 7:056004.
30. Santosa H., Hong M.J., Kim S.-P., Hong K.-S. Noise reduction in functional near-infrared spectroscopy signals by independent component analysis. Rev. Sci. Instrum. 2013, 84:073106.
31. Sitaram R., Zhang H., Guan C., Thulasidas M., Hoshi Y., Ishikawa A., Shimizu K., Birbaumer N. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. NeuroImage 2007, 34:1416-1427.
32. Szollosi D., Denes L.D., Firtha F., Kovacs Z., Fekete A. Comparison of six multiclass classifiers by the use of different classification performance indicators. J. Chemomtr. 2012, 26:76-84.
33. Vuckovic A., Sepulveda F. A two-staged four-class BCI based on imaginary movements of the left and right wrist. Med. Eng. Phys. 2012, 34:964-971.
34. Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 2002, 113:767-791.
35. Wylie G.R., Graber H.L., Voelbel G.T., Kohl A.D., DeLuca J., Pei Y.L., Xu Y., Barbour R.L. Using co-variations in the Hb signal to detect visual activation: a near-infrared spectroscopic imaging study. NeuroImage 2009, 47:473-481.
36. Yarkoni T., Braver T.S. Cognitive neuroscience approaches to individual differences in working memory and executive control: conceptual and methodological issues. Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control 2010, 87-107. Springer, New York. A. Gruszka, G. Matthews, B. Szymuyra (Eds.).