Time–Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery

REFERENCES

• Achard, S., Bassett, D. S., Meyer-Lindenberg, A., & Bullmore, E. (2008). Fractal connectivity of long-memory networks. Physical Review E: Statistical Nonlinear & Soft Matter Physics, 77, 036104.
   PubMed Web of Science ®Google Scholar
• Andrew, C., & Pfurtscheller, G. (1999). Lack of bilateral coherence of post-movement central beta oscillations in the human electroencephalogram. Neuroscience Letters, 273, 89–92.
   PubMed Web of Science ®Google Scholar
• Arvaneh, M., Guan, C., Kai, K. A., Ward, T. E., Chua, K. S. G., & Kuah, C. W. K., et al. (2016). Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement. Neural Computing & Applications. 1–14.
   Web of Science ®Google Scholar
• BCI Competition IV. (2008). Retrieved from http://www.bbci.de/competition/iv/
   Google Scholar
• Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7, 113–40.
   PubMed Web of Science ®Google Scholar
• Boldyreva, G. N., Sharova, E. V., Zhavoronkova, L. A., Chelyapina, M. V., Dubrovskaya, L. P., Simonova, O. A., … Kornienko, V. N. (2014). Structural-functional characteristics of brain functioning on performance and imagination of motor tasks in healthy people (EEG and fMRI studies). Neuroscience & Behavioral Physiology, 44, 731–739.
   Google Scholar
• Chen, C. C., Hsieh, J. C., Wu, Y. Z., Lee, P. L., Chen, S. S., Niddam, D. M., … Wu, Y.-T. (2008). Mutual-information-based approach for neural connectivity during self-paced finger lifting task. Human Brain Mapping, 29, 265–280.
   PubMed Web of Science ®Google Scholar
• Chang, Y., Lee, J., Seo, J., Song, H., Kim, Y., Lee, H. J., … Kim, J. G. (2011). Neural correlates of motor imagery for elite archers. Nmr in Biomedicine, 24, 366–372.
   PubMed Web of Science ®Google Scholar
• Crone, N., Miglioretti, D. B., & Lesser, R. (1998). Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. ii. event-related synchronization in the gamma band. Brain, 121, 2301–2315.
   PubMed Web of Science ®Google Scholar
• Ehrsson, H. H., Geyer, S., & Naito, E. (2003). Imagery of voluntary movement of fingers, toes, and tongue activates corresponding body-part-specific motor representations. Journal of Neurophysiology, 90, 3304–3316.
   PubMed Web of Science ®Google Scholar
• Fallani, F. D. V., Baluch, F., Astolfi, L., Subramanian, D., Zouridakis, G., & Babiloni, F. (2010). Structural organization of functional networks from EEG signals during motor learning tasks. International Journal of Bifurcation & Chaos, 20, 905–912.
   Web of Science ®Google Scholar
• Fallani, F. D. V., Pichiorri, F., Morone, G., Molinari, M., Babiloni, F., Cincotti, F., & Mattia, D. (2013). Multiscale topological properties of functional brain networks during motor imagery after stroke. Neuroimage, 83, 438–449.
   PubMed Web of Science ®Google Scholar
• Ginestet, C. E., & Simmons, A. (2011). Statistical parametric network analysis of functional connectivity dynamics during a working memory task. Neuroimage, 55, 688–704.
   PubMed Web of Science ®Google Scholar
• Graziano, M. (2006). The organization of behavioral repertoire in motor cortex. Annual Review of Neuroscience, 29, 105–134.
   PubMed Web of Science ®Google Scholar
• Grèzes, J., & Decety, J. (2001). Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta-analysis. Human Brain Mapping, 12, 1–19.
   PubMed Web of Science ®Google Scholar
• Guillot, A., & Collet, C. (2005). Duration of mentally simulated movement: a review. Journal of Motor Behavior, 37, 10–20.
   PubMed Web of Science ®Google Scholar
• Guillot, A., Collet, C., Nguyen, V. A., Malouin, F., Richards, C., & Doyon, J. (2008). Functional neuroanatomical networks associated with expertise in motor imagery. Neuroimage, 41, 1471–1483.
   PubMed Web of Science ®Google Scholar
• Guillot, A., Collet, C., Nguyen, V. A., Malouin, F., Richards, C., & Doyon, J. (2009). Brain activity during visual versus kinesthetic imagery: an fMRI study. Human Brain Mapping, 30, 2157–2172.
   PubMed Web of Science ®Google Scholar
• Hanakawa, T., Immisch, I., Toma, K., Dimyan, M. A., Gelderen, P. V., & Hallett, M. (2003). Functional properties of brain regions associated with motor execution and imagery. Journal of Neurophysiology, 89, 989–1002.
   PubMed Web of Science ®Google Scholar
• Hétu, S., Grégoire, M., Saimpont, A., Coll, M. P., Eugène, F., Michon, P. E., & Jackson, P. L. (2013). The neural network of motor imagery: an ALE meta-analysis. Neuroscience & Biobehavioral Reviews, 37, 930–949.
   PubMed Web of Science ®Google Scholar
• Hinault, T., Lemaire, P., & Phillips, N. (2015). Aging and sequential modulations of poorer strategy effects: an EEG study in arithmetic problem solving. Brain Research, 1630, 144–158.
   PubMed Web of Science ®Google Scholar
• Hogg, R. V., & Ledolter, J. (1987). Engineering statistics, 31(787).
   Google Scholar
• Jackson, P. L., Lafleur, M. F., Malouin, F., Richards, C., & Doyon, J. (2001). Potential role of mental practice using motor imagery in neurologic rehabilitation. Archives of Physical Medicine & Rehabilitation, 82, 1133–1141.
   PubMed Web of Science ®Google Scholar
• Jeannerod, M., & Decety, J. (1995). Mental motor imagery: a window into the representational stages of action. Current Opinion in Neurobiology, 5, 727–732.
   PubMed Web of Science ®Google Scholar
• Kanayama, N., Kimura, K., & Hiraki, K. (2015). Cortical EEG components that reflect inverse effectiveness during visuotactile integration processing. Brain Research, 1598, 18–30.
   PubMed Web of Science ®Google Scholar
• Kim, D. H., Kim, L., Park, W., Chang, W. H., Kim, Y. H., Lee, S. W., & Kwon, G. H. (2015). Analysis of time-dependent brain network on active and mi tasks for chronic stroke patients. PLoS One, 10, 65–82.
   Web of Science ®Google Scholar
• Klemm, W. R., Li, T. H., & Hernandez, J. L. (2000). Coherent EEG indicators of cognitive binding during ambiguous figure tasks. Consciousness& Cognition, 9, 66–85.
   PubMed Web of Science ®Google Scholar
• Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., & Krakow, K. (2003). EEG-correlated fMRI of human alpha activity. Neuroimage, 19, 1463–1476.
   PubMed Web of Science ®Google Scholar
• Liu, Z., & He, B. (2008). fMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints. Neuroimage, 39, 1198–1214.
   PubMed Web of Science ®Google Scholar
• Locatelli, T., Cursi, M., Liberati, D., Franceschi, M., & Comi, G. (1998). EEG coherence in alzheimer's disease. Electroencephalography & Clinical Neurophysiology, 106, 229–237.
   PubMedGoogle Scholar
• Lohmann, G., Margulies, D. S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., … Turner, R. (2010). Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS One, 5, e10232.
   PubMed Web of Science ®Google Scholar
• Lu, C. F., Teng, S., Hung, C. I., Tseng, P. J., Lin, L. T., Lee, P. L., & Wu, Y. T. (2011). Reorganization of functional connectivity during the motor task using EEG time–frequency cross mutual information analysis. Clinical Neurophysiology, 122, 1569–1579.
   PubMed Web of Science ®Google Scholar
• Mizuguchi, N., Nakata, H., Uchida, Y., & Kanosue, K. (2012). Motor imagery and sport performance. Journal of Physical Fitness & Sports Medicine, 1, 103–111.
   Google Scholar
• Mühl, C., Gürkök, H., Bos, D. O., Thurlings, M. E., Scherffig, L., Duvinage, M., … Heylen, D. K. J. (2010). Bacteria hunt: a multimodal, multiparadigm BCI game. Presented at the Fifth International Summer Workshop on Multimodal Interfaces, Genoa, Italy.
   Google Scholar
• Muldoon, S. F., Bridgeford, E. W., & Bassett, D. S. (2015). Small-world propensity in weighted, real-world networks. Quantitative Biology, 1–13.
   Google Scholar
• Müller, K. R., Krauledat, M., Dornhege, G., Curio, G., & Blankertz, B. (2004). Machine learning techniques for brain-computer interfaces. In M. J. Smith & G. Salvendy (Eds.), Human interface and management of information. Methods, techniques, and tools in information design. Human Interface 2007. Lecture notes in computer science, vol. 4557 (pp. 705–714). Berlin, Germany: Springer.
   Google Scholar
• Munzert, J., Lorey, B., & Zentgraf, K. (2009). Cognitive motor processes: the role of motor imagery in the study of motor representations. Brain Research Reviews, 60, 306–326.
   PubMed Web of Science ®Google Scholar
• Nick, T. G. (1994). Principles of biostatistics [Book review]. American Statistician, 48, 266.
   Google Scholar
• Personnier, P., Ballay, Y., & Papaxanthis, C. (2010). Mentally represented motor actions in normal aging: iii.electromyographic features of imagined arm movements. Behavioural Brain Research, 206, 184–191.
   PubMed Web of Science ®Google Scholar
• Pfurtscheller, G. (1992). Event-related synchronization (ERS): an electrophysiological correlate of cortical regions at rest. Electroencephalography & Clinical Neurophysiology, 83, 62–69.
   PubMedGoogle Scholar
• Pfurtscheller, G., Brunner, C., Schlögl, A., & Silva, F. H. L. D. (2006). Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage, 31, 153–159.
   PubMed Web of Science ®Google Scholar
• Popivanov, D., & Dushanova, J. (1999). Non-linear EEG dynamic changes and their probable relation to voluntary movement organization. Neuroreport, 10, 1397–1401.
   PubMed Web of Science ®Google Scholar
• Popivanov, D., Mineva, A., & Dushanova, J. (1999). Dynamic characteristics of laser-Doppler flux data. Technology & Health Care Official Journal of the European Society for Engineering & Medicine, 7, 205–218.
   PubMedGoogle Scholar
• Rugg, M. D., & Curran, T. (2007). Event-related potentials and recognition memory. Trends in Cognitive Sciences, 11, 251–257.
   PubMed Web of Science ®Google Scholar
• Schuster, C., Hilfiker, R., Amft, O., Scheidhauer, A., Andrews, B., Butler, J., … Ettlin, T. (2011). Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines. Bmc Medicine, 9, 75.
   PubMed Web of Science ®Google Scholar
• Sporns, O., Honey, C. J., & Kötter, R. (2007). Identification and classification of hubs in brain networks. PLoS One, 2, e1049.
   PubMed Web of Science ®Google Scholar
• Thach, W. T., & Bastian, A. J. (2004). Role of the cerebellum in the control and adaptation of gait in health and disease. Progress in Brain Research, 143, 353–366.
   PubMed Web of Science ®Google Scholar
• Tung, S. W., Guan, C., Kai, K. A., Phua, K. S., Wang, C., Kuah, C. W. K., … Chew, E. (2015, December). A measurement of motor recovery for motor imagery-based BCI using EEG coherence analysis. Paper presented at the International Conference on Information, Communications and Signal Processing, Singapore.
   Google Scholar
• Xia, M., Wang, J., & He, Y. (2013). Brainnet viewer: a network visualization tool for human brain connectomics. PLoS One, 8, e68910.
   PubMed Web of Science ®Google Scholar
• Yin, X., Xu, B., Jiang, C., Fu, Y., Wang, Z., Li, H., & Shi, G. (2015). A hybrid BCI based on EEG and FNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching. Journal of Neural Engineering, 12, 036004.
   PubMed Web of Science ®Google Scholar
• Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks. Neuroimage, 53, 1197–1207.
   PubMed Web of Science ®Google Scholar
• Zou, B., Liu, Y., Guo, M., & Wang, Y. (2015). EEG-based assessment of stereoscopic 3D visual fatigue caused by vergence-accommodation conflict. Journal of Display Technology, 11, 1076–1083.
   Google Scholar