Friend engine framework: A real time neuro feedback client-server system for neuro imaging studies

Frontiers in Behavioral Neuroscience

Volumn 9, Issue JAN, 2015, Pages

  Basilio, Rodrigo ^a   Garrido, Griselda J ^a   Sato, João R ^a,b   Hoefle, Sebastian ^a   Melo, Bruno R P ^a   Pamplona, Fabricio A ^a   Zahn, Roland ^c   Moll, Jorge ^a  

^a D OR INSTITUTE FOR RESEARCH AND EDUCATION IDOR   (Brazil)

^b UNIVERSIDADE FEDERAL DO ABC   (Brazil)

^c KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Brain computer interface (BCI); EEG; FSL; Neuro feedback; Real time fMRI

Indexed keywords

ARTICLE; BOLD SIGNAL; BRAIN COMPUTER INTERFACE; COMMUNICATION PROTOCOL; COMPUTER INPUT DEVICE; COMPUTER INTERFACE; COMPUTER LANGUAGE; CONCEPTUAL FRAMEWORK; DATA PROCESSING; ELECTROENCEPHALOGRAM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN COMPUTER INTERACTION; IMAGING SOFTWARE; NEUROFEEDBACK; NEUROIMAGING; PROCESS DESIGN; WORKFLOW;

EID: 84922227110     PISSN: 16625153     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnbeh.2015.00003     Document Type: Article

References (21)

1. Chang, C.-C., and Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 27, 1-27. doi: 10.1145/1961189.1961199
2. Cohen, O., Koppel, M., Malach, R., and Friedman, D. (2014). Controlling an avatar by thought using real-time fMRI. J. Neural Eng. 11:035006. doi: 10.1088/17412560/11/3/035006
3. Cox, R. W. (1996). AFNI: Software for analysis andvisualization offunctional magnetic resonance neuroimages. Comput. Biomed. Res. Int. J. 29, 162-173. doi: 10.1006/cbmr.1996.0014
4. Goebel, R. (2012). BrainVoyager-past, present, future. Neuroimage 62, 748-756. doi: 10.1016/j.neuroimage.2012.01.083
5. Goebel, R., Sorger, B., Kaiser, J., Birbaumer, N., and Weiskopf, N. (2004). BOLD Brain Pong: Self-Regulation of Local Brain Activity During Synchronously Scanned, Interacting Subjects. San Diego, CA: Society for Neuroscience.
6. Green, S., Ralph, M. A. L., Moll, J., Deakin, J. F. W., and Zahn, R. (2012). Guilt-selective functional disconnection of anterior temporal and subgenual cortices in major depressive disorder. Arch. Gen. Psychiatry 69, 1014-1021. doi: 10.1001/archgenpsychiatry.2012.135
7. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., and Smith, S. M. (2012). FSL. Neuroimage 62, 782-790. doi: 10.1016/j.neuroimage.2011.09.015
8. LaConte, S. M. (2011). Decoding fMRI brain states in real-time. Neuroimage 56, 440-454. doi: 10.1016/j.neuroimage.2010.06.052
9. LaConte, S. M., Peltier, S. J., and Hu, X. P. (2007). Real-time fMRI using brain-state classification. Hum. BrainMapp. 28, 1033-1044. doi: 10.1002/hbm.20326
10. Moll, J., Weingartner, J. H., Bado, P., Basilio, R., Sato, J. R., Melo, B. R., et al. (2014). Voluntary enhancement of neural signatures of affiliative emotion using FMRI neuro feedback. PLoS ONE 9:e97343. doi: 10.1371/journal.pone.0097343
11. Rana, M., Gupta, N., Dalboni Da Rocha, J. L., Lee, S., and Sitaram, R. (2013). A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals. Front. Neurosci. 7:170. doi: 10.3389/fnins.2013.00170
12. Renaud, P., Joyal, C., Stoleru, S., Goyette, M., Weiskopf, N., and Birbaumer, N. (2011). Real-time functional magnetic imaging-brain-computer interface and virtual reality promising tools for the treatment of pedophilia. Prog. Brain Res. 192, 263-272. doi: 10.1016/B978-0-444-53355-5.00014-2
13. Sato, J. R., Basilio, R., Paiva, F. F., Garrido, G. J., Bramati, I. E., Bado, P., et al. (2013). Real-time fMRI pattern decoding and neurofeedback using FRIEND: An FSL-integrated BCI toolbox. PLoS ONE 8:e81658. doi: 10.1371/jour-nal.pone.0081658
14. Sato, J. R., Thomaz, C. E., Cardoso, E. F., Fujita, A., Martin, M. da G., and Amaro, E. (2008). Hyperplane navigation: A method to set individual scores in fMRI group datasets. Neuroimage 42, 1473-1480. doi: 10.1016/j.neuroimage.2008.06.024
15. Sitaram, R., Lee, S., Ruiz, S., Rana, M., Veit, R., and Birbaumer, N.(2011). Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage 56, 753-765. doi: 10.1016/j.neuroimage.2010.08.007
16. Sorger, B., Reithler, J., Dahmen, B., and Goebel, R. (2012). A real-time fMRI-based spelling device immediately enabling robust motor-independent communication. Curr. Biol. 22, 1333-1338. doi: 10.1016/j.cub.2012.05.022
17. Stoeckel, L. E., Garrison, K. A., Ghosh, S., Wighton, P., Hanlon, C. A., Gilman, J. M., et al. (2014). Optimizing real time fMRI neurofeedback for therapeutic discovery and development. Neuroimage. 5, 254-255. doi: 10.1016/j.nicl.2014.07.002
18. Stroustrup, B. (1988). What is object-oriented programming? IEEE Softw. 5,10-20. doi: 10.1109/52.2020
19. Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L., et al. (2013). Real-time fMRI neuro feed back: Progress and challenges. Neuroimage 76, 386-399. doi: 10.1016/j.neuroimage.2013.03.033
20. Weiskopf, N. (2012). Real-time fMRI and its application to neurofeedback. Neuroimage 62, 682-692. doi: 10.1016/j.neuroimage.2011.10.009
21. Zotev, V., Krueger, F., Phillips, R., Alvarez, R. P., Simmons, W. K., Bellgowan, P., et al. (2011). Self-regulation of amygdala activation using real-time FMRI neurofeedback. PLoS ONE 6:e24522. doi: 10.1371/journal.pone.0024522