Abstract
Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain–computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain–computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing.
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Notes
Note that the absolute classification bias is different from the bias correction term (see Online data processing in the BCI task). The absolute classification bias is measured as classifier performance, where the bias correction term constitutes the classifier decision boundary.
References
Bahramisharif A, van Gerven M, Heskes T, Jensen O (2010) Covert attention allows for continuous control of brain-computer interfaces. Eur J Neurosci 31:1501–1508. doi:10.1111/j.1460-9568.2010.07174.x
Bastiaansen MC, Knösche TR (2000) Tangential derivative mapping of axial MEG applied to event-related desynchronization research. Clin Neurophysiol 111:1300–1305
Brainard DH (1997) The Psychophysics Toolbox. Spat Vis 10:433–436
Doppelmayr M, Klimesch W, Pachinger T, Ripper B (1998) Individual differences in brain dynamics: important implications for the calculation of event-related band power. Biol Cybern 79:49–57
Foxe JJ, Snyder AC (2011) The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Front Psychol 2:154. doi:10.3389/fpsyg.2011.00154
Fuchs T, Birbaumer N, Lutzenberger W et al (2003) Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate. Appl Psychophysiol Biofeedback 28:1–12
Gould IC, Rushworth MF, Nobre AC (2011) Indexing the graded allocation of visuospatial attention using anticipatory alpha oscillations. J Neurophysiol 105:1318–1326. doi:10.1152/jn.00653.2010
Guger C, Ramoser H, Pfurtscheller G (2000) Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Trans Rehabil Eng 8:447–456. doi:10.1109/86.895947
Haegens S, Händel BF, Jensen O (2011) Top-down controlled alpha band activity in somatosensory areas determines behavioral performance in a discrimination task. J Neurosci 31:5197–5204. doi:10.1523/JNEUROSCI.5199-10.2011
Händel BF, Haarmeier T, Jensen O (2011) Alpha oscillations correlate with the successful inhibition of unattended stimuli. J Cogn Neurosci 23:2494–2502. doi:10.1162/jocn.2010.21557
Horschig JM, Jensen O, van Schouwenburg MR et al (2014a) Alpha activity reflects individual abilities to adapt to the environment. NeuroImage 89:235–243. doi:10.1016/j.neuroimage.2013.12.018
Horschig JM, Zumer JM, Bahramisharif A (2014b) Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations. Front Syst Neurosci 8:119. doi:10.3389/fnsys.2014.00119
Jensen O, Mazaheri A (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci 4:186. doi:10.3389/fnhum.2010.00186
Jensen O, Bahramisharif A, Oostenveld R et al (2011) Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience. Front Psychol 2:100. doi:10.3389/fpsyg.2011.00100
Jensen O, Bonnefond M, VanRullen R (2012) An oscillatory mechanism for prioritizing salient unattended stimuli. Trends Cogn Sci 16:200–206. doi:10.1016/j.tics.2012.03.002
Kelly SP, Lalor EC, Reilly RB, Foxe JJ (2005) Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Trans Neural Syst Rehabil Eng 13:172–178. doi:10.1109/TNSRE.2005.847369
Kelly SP, Gomez-Ramirez M, Foxe JJ (2009) The strength of anticipatory spatial biasing predicts target discrimination at attended locations: a high-density EEG study. Eur J Neurosci 30:2224–2234. doi:10.1111/j.1460-9568.2009.06980.x
Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev 29:169–195
Klimesch W (2012) α-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci 16:606–617. doi:10.1016/j.tics.2012.10.007
Kübler A, Neumann N, Wilhelm B et al (2004) Predictability of Brain-Computer Communication. J Psychophysiol 18:121–129. doi:10.1027/0269-8803.18.23.121
Lotte F, Congedo M, Lécuyer A et al (2007) A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 4:R1–R13. doi:10.1088/1741-2560/4/2/R01
McFarland DJ, Sarnacki WA, Wolpaw JR (2003) Brain-computer interface (BCI) operation: optimizing information transfer rates. Biol Psychol 63:237–251
Meeuwissen EB, Takashima A, Fernández G, Jensen O (2011) Increase in posterior alpha activity during rehearsal predicts successful long-term memory formation of word sequences. Hum Brain Mapp 32:2045–2053. doi:10.1002/hbm.21167
Obermaier B, Neuper C, Guger C, Pfurtscheller G (2001) Information transfer rate in a five-classes brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 9:283–288. doi:10.1109/7333.948456
Oostenveld R, Fries P, Maris E, Schoffelen J-M (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011:156869. doi:10.1155/2011/156869
Park H, Lee DS, Kang E et al (2014) Blocking of irrelevant memories by posterior alpha activity boosts memory encoding. Hum Brain Mapp. doi:10.1002/hbm.22452
Pelli DG (1997) The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat Vis 10:437–442
Pfurtscheller G, Neuper C, Guger C et al (2000) Current trends in Graz brain-computer interface (BCI) research. IEEE Trans Rehabil Eng 8:216–219. doi:10.1109/86.847821
Posner MI (1980) Orienting of attention. Q J Exp Psychol 32:3–25. doi:10.1080/00335558008248231
Rihs TA, Michel CM, Thut G (2007) Mechanisms of selective inhibition in visual spatial attention are indexed by alpha-band EEG synchronization. Eur J Neurosci 25:603–610. doi:10.1111/j.1460-9568.2007.05278.x
Stolk A, Todorovic A, Schoffelen J-M, Oostenveld R (2013) Online and offline tools for head movement compensation in MEG. NeuroImage 68:39–48. doi:10.1016/j.neuroimage.2012.11.047
Sykacek P, Roberts SJ, Stokes M (2004) Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation. IEEE Trans Biomed Eng 51:719–727. doi:10.1109/TBME.2004.824128
Ter Huurne N, Onnink M, Kan C et al (2013) Behavioral consequences of aberrant alpha lateralization in attention-deficit/hyperactivity disorder. Biol Psychiatry 74:227–233. doi:10.1016/j.biopsych.2013.02.001
Thut G, Nietzel A, Brandt SA, Pascual-Leone A (2006) Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J Neurosci 26:9494–9502. doi:10.1523/JNEUROSCI.0875-06.2006
Tonin L, Leeb R, Del R, Millán J (2012) Time-dependent approach for single trial classification of covert visuospatial attention. J Neural Eng 9:045011. doi:10.1088/1741-2560/9/4/045011
Tonin L, Leeb R, Sobolewski A, del Millán J (2013) An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation. J Neural Eng 10:056007. doi:10.1088/1741-2560/10/5/056007
Treder MS, Bahramisharif A, Schmidt NM et al (2011) Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention. J Neuroeng Rehabil 8:24. doi:10.1186/1743-0003-8-24
Van Dongen-Boomsma M, Vollebregt MA, Slaats-Willemse D, Buitelaar JK (2013) A randomized placebo-controlled trial of electroencephalographic (EEG) neurofeedback in children with attention-deficit/hyperactivity disorder. J Clin Psychiatry 74:821–827. doi:10.4088/JCP.12m08321
Van Gerven M, Bahramisharif A, Heskes T, Jensen O (2009) Selecting features for BCI control based on a covert spatial attention paradigm. Neural Netw 22:1271–1277. doi:10.1016/j.neunet.2009.06.004
Vollebregt MA, van Dongen-Boomsma M, Buitelaar JK, Slaats-Willemse D (2013) Does EEG-neurofeedback improve neurocognitive functioning in children with attention-deficit/hyperactivity disorder? A systematic review and a double-blind placebo-controlled study. J Child Psychol Psychiatry 55:460–472. doi:10.1111/jcpp.12143
Worden MS, Foxe JJ, Wang N, Simpson GV (2000) Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J Neurosci 20:RC63
Acknowledgments
The authors gratefully acknowledge the support of the BrainGain Smart Mix Programme of the Netherlands Ministry of Economic Affairs and The Netherlands Ministry of Education, Culture and Science.O.J. is supported by the research program “The healthy brain” funded by The Netherlands Initiative Brain and Cognition (NIHC), a part of the Organization for Scientific Research (NWO) under Grant number 056-14-011 and by a VICI grant under number 453-09-002.
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Horschig, J.M., Oosterheert, W., Oostenveld, R. et al. Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain–Computer Interface. Brain Topogr 28, 852–864 (2015). https://doi.org/10.1007/s10548-014-0401-7
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DOI: https://doi.org/10.1007/s10548-014-0401-7