Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

Frontiers in Human Neuroscience

Volumn 8, Issue 1 APR, 2014, Pages

  Jawad Khan, M ^a   Hong, Melissa Jiyoun ^b   Hong, Keum Shik ^a  

^a Pusan National University   (South Korea)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Arithmetic mental task; Electroencephaelography; Hybrid brain computer interface; Linear discriminant analysis; Motor execution; Near infrared spectroscopy

Indexed keywords

ACCURACY; ADULT; ARTICLE; BRAIN COMPUTER INTERFACE; BRAIN FUNCTION; BRAIN REGION; ELECTROENCEPHALOGRAPHY; EXECUTIVE FUNCTION; HUMAN; HUMAN EXPERIMENT; LEFT HEMISPHERE; MALE; MENTAL TASK; MOTOR CONTROL; MOTOR COORDINATION; MOTOR CORTEX; NEAR INFRARED SPECTROSCOPY; NORMAL HUMAN; PREFRONTAL CORTEX; RIGHT HEMISPHERE;

EID: 84899704224     PISSN: None     EISSN: 16625161     Source Type: Journal    
DOI: 10.3389/fnhum.2014.00244     Document Type: Article

References (59)

1. Ball, T., Kern, M., Mutschler, I., Aertsen, A., and Schulze-Bonhage, A. (2009). Signal quality of simultaneously recorded invasive and non-invasive EEG. Neuroimage 46, 708-716. doi: 10.1016/j.neuroimage.2009.02.028
2. Beisteiner, R., Höllinger, P., Lindinger, G., Lang, W., and Berthoz, A. (1995). Mental representations of movements. Brain potentials associated with imagination of hand movements. Electroencephalogr. Clin. Neurophysiol. 96, 183-193. doi: 10.1016/0168-5597(94)00226-5
3. Bhutta, M. R., Hong, K.-S., Kim, B.-M., Hong, M.-J., Kim, Y.-H., and Lee, S.-H. (2014). Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water. Rev. Sci. Instrum. 85, 1-3. doi: 10.1063/1.4865124
4. Carlson, T., and Millán, J. R. (2013). Brain-controlled wheelchairs: a robotic architecture. IEEE Robot. Automat. Mag. 20, 65-73. doi: 10.1109/MRA.2012.2229936
5. Coyle, S. M., Ward, T. E., and Markham, C. M. (2007). Brain-computer interface using a simplified functional near-infrared spectroscopy system. J. Neural Eng. 3, 219-226. doi: 10.1088/1741-2560/4/3/007
6. Delorme, A., and Makeig, S. (2004). EEGlab: an open source toolbox for analysis of single trail EEG dynamics. J. Neurosci. Methods 134, 9-21. doi: 10.1016/j.jneumeth.2003.10.009
7. Fattouh, A., Horn, O., and Bourhis, G. (2013). Emotional BCI control of a smart wheelchair. Int. J. Comput. Sci. 10, 32-36.
8. Fazli, S., Mehnert, J., Steinbrink, J., Curio, G., Villringer, A., Müller, K.-R., et al. (2012). Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage 59, 512-529. doi: 10.1016/j.neuroimage.2011.07.084
9. Galán, F., Nuttin, M., Lew, E., Ferrez, P. W., Vanacker, G., Philips, J., et al. (2008). A brain-actuated wheelchair: asynchronous and non-invasive brain-computer interfaces for continuos control of robots. Clin. Neurohysiol. 119, 2159-2169. doi: 10.1016/j.clinph.2008.06.001
10. Gervain, J., Mehler, J., Werker, J. F., Nelson, C. A., Csibra, G., Lloyd-Fox, S., et al. (2011). Near-infrared spectroscopy: a report from the McDonnell infant methodology consortium. Dev. Cogn. Neurosci. 1, 22-46. doi: 10.1016/j.dcn.2010.07.004
11. Gneo, M., Severini, G., Conforto, S., Schmid, M., and D'Alessio, T. (2011). Towards a brain-activated and eye-controlled wheel chair. Int. J. Bioeletromagn. 13, 44-45.
12. Homan, R. W., Herman, J., and Purdy, P. (1987). Cerebral location of international 10-20 system electrode placement. Electroencephalogr. Clin. Neurophysiol. 66, 376-382. doi: 10.1016/0013-4694(87)90206-9
13. Hu, X.-S., Hong, K.-S., and Ge, S. S. (2011). Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series. Neurosci. Lett. 504, 115-120. doi: 10.1016/j.neulet.2011.09.011
14. Hu, X.-S., Hong, K.-S., and Ge, S. S. (2012). fNIRS-based online deception decoding. J. Neural Eng. 9:2. doi: 10.1088/1741-2560/9/2/026012
15. Hu, X.-S., Hong, K.-S., and Ge, S. S. (2013). Reduction of trail-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity. J. Biomed. Opt. 18, 1-9. doi: 10.1117/1.JBO.18.1.017003
16. Hu, X.-S., Hong, K.-S., Ge, S. S., and Jeong, M.-Y. (2010). Kalman estimator-and general linear model-based on-line brain activation mapping by near-infrared spectroscopy. Biomed. Eng. Online 9:82. doi: 10.1186/1475-925X-9-82
17. Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., and Bai, O. (2012). Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchornization/synchronization and state control. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 379-388. doi: 10.1109/TNSRE.2012.2190299
18. Irani, F., Platek, S. M., Bunce, S., Ruocco, A. C., and Chute, D. (2007). Functional near-infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. Neuropsychologist 21, 9-37. doi: 10.1080/13854040600910018
19. Jochumsen, M., Niazi, I. K., Mrachacz-Kersting, N., Farina, D., and Dremstrup, K. (2013). Detection and classification of movement-related cortical potentials associated with task forece and speed. J. Neural Eng. 10, 5, 1-9. doi: 10.1088/1741-2560/10/5/056015
20. Jurcak, V., Tsuzuki, D., and Dan, I. (2007). 10/20, 10/10, and 10/5 system revisited: their validity as head-surface-based positioning system. Neuroimage 34, 1600-1611. doi: 10.1016/j.neuroimage.2006.09.024
21. Kaiser, V., Bauernfeind, G., Kreilinger, A., Kaufmann, T., Kubler, A., Neuper, C., et al. (2014). Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG. Neuroimage 85, 432-444. doi: 10.1016/j.neuroimage.2013.04.097
22. Kamran, M. A., and Hong, K.-S. (2013). Linear parameter varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study. J. Neural Eng. 10:5. doi: 10.1088/1741-2560/10/5/056002
23. Knyazev, G. G. (2013). EEG correlates of self-referential processing. Front. Hum. Neurosci. 7:264. doi: 10.3389/fnhum.2013.00264
24. Li, Y. Q., Pan, J. H., Wang, F., and Yu, Z. (2013). A hybrid BCI system combining P300 and SSVEP and its application to wheel chair control. IEEE Trans. Biomed. Eng. 60, 3156-3166. doi: 10.1109/TBME.2013.2270283
25. Lin, J.-S., and Yang, J.-S. (2012). Wireless brian-computer interface for electric wheelcahirs with EEG and eye-blinking signals. Int. J. Innovat. Comput. Inform. Control 8, 6011-6024.
26. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., and Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4:2. doi: 10.1088/1741-2560/4/2/R01
27. Matsuyama, H., Asama, H., and Otake, M. (2009). "Design of differential near-infrared spectroscopy based brain machine interface, " in IEEE International Symposium on Robot and Human Interactive Communication, (Toyama), 775-780. doi: 10.1109/ROMAN.2009.5326215
28. Matthews, F., Pearlmutter, B. A., Ward, T. E., Soraghan, C., and Markham, C. (2008). Hemodynamics for brain computer interfaces. IEEE Signal Process. Mag. 25, 87-94. doi: 10.1109/MSP.2008.4408445
29. Min, B. K., Marzelli, M. J., and Yoo, S.-S. (2010). Neuroimaging-based approaches in brain-computer interface. Trends Biotechnol. 28, 552-560. doi: 10.1016/j.tibtech.2010.08.002
30. Nagdyman, N., Fleck, T. P. K., Ewert, P., Abdul-Khaliq, H., Redlin, M., and Lange, P. E. (2003). Cerebral oxygenation measured by near-infrared spectroscopy during circulatory arrest and cardiopulmonary resuscitation. Br. J. Anesth. 91, 438-442. doi: 10.1093/bja/aeg181
31. Naito, M., Michioka, Y., Ozawa, K., Ito, Y., Kiguchi, H., and Kanazaw, T. (2007). A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Trans. Infrom. Syst. 90, 1028-1037. doi: 10.1093/ietisy/e90-d.7.1028
32. Naseer, N., and Hong, K.-S. (2013). Classification of functional near-infrared spectroscopy signals corresponding to right-and left-wrist motor imagery for development of a brain-computer interface. Neurosci. Lett. 553, 84-89. doi: 10.1016/j.neulet.2013.08.021
33. Naseer, N., Hong, M. J., and Hong, K.-S. (2014). Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface. Exp. Brain Res. 232, 555-564. doi: 10.1007/s00221-013-3764-1
34. Nicolas-Alonso, L. F., and Gomez-Gil, J. (2012). Brain-computer interfaces, a review. Sensors 12, 1211-1279. doi: 10.3390/s120201211
35. Niedermeyer, E., and Lopes da Silva, F. H. (1999). Electroencephalography: Basic Principles, Clinical Applications and Related Fields. Philadelphia, PA: Lippincott William and Wilkins.
36. Ortiz-Rosario, A., and Adeli, H. (2013). Brain-computer interface technologies: from signal to action. Rev. Neurosci. 24, 537-552. doi: 10.1515/revneuro-2013-0032
37. Pfurtscheller, G., Allison, B. Z., Brunner, C., Bauernfeind, G., Solis-Escalante, T., Scherer, R., et al. (2010). The hybrid BCI. Front. Neurosci. 4:30. doi: 10.3389/fnpro.2010.00003
38. Pivick, R. T., Broughton, R. J., Coppola, R., Davidson, R. J., Fox, N., and Nuwer, M. R. (1993). Guidelines for recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology30, 547-558. doi: 10.1111/j.1469-8986.1993.tb02081.x
39. Porro, C. A., Francescato, M. P., Cettolo, V., Diamond, M. E., Baraldi, P., Zuiani, C., et al. (1996). Primay motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study. J. Neurosci. 16, 7688-7698.
40. Power, S. D., Falk, T. H., and Chau, T. (2010). Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy. J. Neural Eng. 7:2. doi: 10.1088/1741-2560/7/2/026002
41. Power, S. D., Khushki, A., and Chau, T. (2012). Automatic single-trial discrimination of mental arithmetic, mental singing and no-control state form the prefrontal activity: towards the three state NIRS-BCI. BMC Res. Notes 5:141. doi: 10.1186/1756-0500-5-141
42. Rehan, M., and Hong, K.-S. (2012). Robust synchronization of delayed chaotic FitzHugh-Nagumo neurons under external electrical stimulation. Comput. Math. Method Med. 2012, 1-11. doi: 10.1155/2012/230980
43. Rehan, M., and Hong, K.-S. (2013). Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. PLoS ONE 8:e62888. doi: 10.1371/journal.pone.0062888
44. Robinson, N., Vinod, A. P., Ang, K. K., Tee, K. P., and Guan, C. T. (2013). EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm. IEEE Trans. Biomed. Eng. 60, 2123-2132. doi: 10.1109/TBME.2013.2248153
45. Safaie, J., Grebe, R., Moghaddam, H. A., and Wallois, F. (2013). Towards a fully integrated wearable EEG-NIRS bimodal acquisition system. J. Neural Eng. 10:5. doi: 10.1088/1741-2560/10/5/056001
46. Santosa, H., Hong, M. J., Kim, S.-P., and Hong, K.-S. (2013). Noise reduction in functional near-infrared spectroscopy signals by independent component analysis. Rev. Sci. Instrum. 84:7. doi: 10.1063/1.4812785
47. Stangl, M., Bauernfeind, G., Kurzmann, J., Scherer, R., and Neuper, C. (2013). A heamodynamic brain-computer interface based on real-time classification of near infrared spectroscopy signals during motor imagery and mental arithmetic. J. Near Infrared Spectrosc. 21, 157-171. doi: 10.1255/jnirs.1048
48. Subasi, A., and Gursoy, M. I. (2010). EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37, 8659-8666. doi: 10.1016/j.eswa.2010.06.065
49. Turnip, A., and Hong, K.-S. (2012). Classifying mental activities from EEG-P300 signals using adaptive neural network. Int. J. Innovat. Comput. Inform. Control 8, 6429-6443.
50. Turnip, A., Hong, K.-S., and Jeong, M.-Y. (2011). Real-time feature extraction of EEG-based P300 using adaptive nonlinear principal component analysis. Biomed. Eng. Online 10:83. doi: 10.1186/1475-925X-10-83
51. Verner, M., Herrmann, M. J., Troche, S. J., Robers, C. M., and Rammsyaer, T. M. (2013). Cortical oxygen consumption in mental arithmetic as a function task difficulty: a near-infrared spectroscopy approach. Front. Hum. Neurosci. 7:217. doi: 10.3389/fnhum.2013.00217
52. Vidaurre, C., and Blankertz, B. (2010). Towards a cure for BCI illiteracy. Brain Topogr. 23, 194-198. doi: 10.1007/s10548-009-0121-6
53. Vuckovic, A., and Sepulveda, F. (2012). A two-stage four-class BCI based on imagenary movements of the left and right wrist. Med. Eng. Phys. 34, 964-971. doi: 10.1016/j.medengphy.2011.11.001
54. Waldert, S., Tüshaus, L., Kaller, C. P., Aertsen, A., and Mehring, C. (2012). fNIRS exhibits weak tuning to hand movement direction. PLoS ONE 7:e49266. doi: 10.1371/journal.pone.0049266
55. Wolpaw, J. R., and McFarland, D. J. (2004). Control of two-dimensional movement signal by a noninvasive brain-comuter interface in humans. Proc. Natl. Acad. Sci. U.S.A. 101, 17849-17852. doi: 10.1073/pnas.0403504101
56. Xu, M. P., Qi, H. Z., Wan, B. K., Yin, T., Liu, Z. P., and Ming, D. (2013). A hybrid BCI speller paradigm combining P300 potential and SSVEP blocking feature. J. Neural Eng. 10:2. doi: 10.1088/1741-2560/10/2/026001
57. Ye, J. C., Tak, S., Jang, K. E., Jung, J., and Jang, J. (2009). NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy. Neuroimage 44, 428-447. doi: 10.1016/j.neuroimage.2008.08.036
58. Zaepffel, M., Trachel, R., Kilavik, B. E., and Brochier, T. (2013). Modulation of EEG beta power during planning and execution of grasping movements. PLoS ONE 8:e60060. doi: 10.1371/journal.pone.0060060
59. Zhang, R., Xu, P., Guo, L., Zhang, Y., Li, P., and Yao, D. (2013). Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS ONE 8:e74433. doi: 10.1371/journal.pone.0074433