Startseite Tracking markers of response inhibition in electroencephalographic data: why should we and how can we go beyond the N2 component?
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Tracking markers of response inhibition in electroencephalographic data: why should we and how can we go beyond the N2 component?

  • Marion Albares , Guillaume Lio und Philippe Boulinguez EMAIL logo
Veröffentlicht/Copyright: 25. April 2015
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Abstract

Response inhibition is a pivotal component of executive control, which is especially difficult to assess. Indeed, it is a substantial challenge to gauge brain-behavior relationships because this function is precisely intended to suppress overt measurable behaviors. A further complication is that no single neuroimaging method has been found that can disentangle the accurate time-course of concurrent excitatory and inhibitory mechanisms. Here, we argue that this objective can be achieved with electroencephalography (EEG) on some conditions. Based on a systematic review, we emphasize that the standard event-related potential N2 (N200) is not an appropriate marker of prepotent response inhibition. We provide guidelines for assessing the cortical brain dynamics of response inhibition with EEG. This includes the combined use of inseparable data processing steps (source separation, source localization, and single-trial and time-frequency analyses) as well as the amendment of the classical experimental designs to enable the recording of different kinds of electrophysiological activity predicted by different models of response inhibition. We conclude with an illustration based on recent findings of how fruitful this approach can be.


Corresponding author: Philippe Boulinguez, CNRS, UMR5229, Centre de Neuroscience Cognitive, 67 Bd Pinel, F-69675 Bron cedex, France, e-mail: ; Université de Lyon, F-69622 Lyon, France; and Université Lyon 1, F-69622 Villeurbanne, France

Acknowledgments

The authors declare no competing financial interests. This work was supported by a grant from Agence Nationale de la Recherche (ANR-MNPS-039-01), Institut de France/Fondation NRJ, and Labex CORTEX. We express our thanks to the Centre Hospitalier St Jean de Dieu for promoting this research program.

References

Albares, M., Criaud, M., Wardak, C., Nguyen, S.C., Ben Hamed, S., and Boulinguez, P. (2011). Attention to baseline: does orienting visuospatial attention really facilitate target detection? J. Neurophysiol. 106, 809–816.10.1152/jn.00206.2011Suche in Google Scholar PubMed

Albares, M., Lio, G., Criaud, M., Anton, J.L., Desmurget, M., and Boulinguez, P. (2014a). The dorsal medial frontal cortex mediates automatic motor inhibition in uncertain contexts: evidence from combined fMRI and EEG studies. Hum. Brain Mapp. 35, 5517–5531.10.1002/hbm.22567Suche in Google Scholar PubMed PubMed Central

Albares, M., Thobois, S., Favre, E., Broussolle, E., Polo, G., Domenech, P., Boulinguez, P., and Ballanger, B. (2014b). Interaction of noradrenergic pharmacological manipulation and subthalamic stimulation on movement initiation control in Parkinson’s disease. Brain Stimul. 8, 27–35.10.1016/j.brs.2014.09.002Suche in Google Scholar PubMed

Albert, J., López-Martín, S., Hinojosa, J.A., and Carretié, L. (2013). Spatiotemporal characterization of response inhibition. Neuroimage 76, 272–281.10.1016/j.neuroimage.2013.03.011Suche in Google Scholar PubMed

Aron, A.R. (2011). From reactive to proactive and selective control: developing a richer model for stopping inappropriate responses. Biol. Psychiatry 69, e55–e68.10.1016/j.biopsych.2010.07.024Suche in Google Scholar PubMed PubMed Central

Aron, A.R. and Poldrack, R.A. (2005). The cognitive neuroscience of response inhibition: relevance for genetic research in attention-deficit/hyperactivity disorder. Biol. Psychiatry 57, 1285–1292.10.1016/j.biopsych.2004.10.026Suche in Google Scholar PubMed

Attal, Y., Maess, B., Friederici, A., and David, O. (2012). Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic data. Rev. Neurosci. 23, 85–95.10.1515/rns.2011.056Suche in Google Scholar PubMed

Ballanger, B., van Eimeren, T., Moro, E., Lozano, A.M., Hamani, C., Boulinguez, P., Pellecchia, G., Houle, S., Poon, Y.Y., Lang, A.E., et al. (2009). Stimulation of the subthalamic nucleus and impulsivity: release your horses. Ann. Neurol. 66, 817–824.10.1002/ana.21795Suche in Google Scholar PubMed PubMed Central

Bari, A. and Robbins, T.W. (2013). Inhibition and impulsivity: behavioral and neural basis of response control. Prog. Neurobiol. 108, 44–79.10.1016/j.pneurobio.2013.06.005Suche in Google Scholar PubMed

Barkley, R.A. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol. Bull. 121, 65–94.10.1037/0033-2909.121.1.65Suche in Google Scholar PubMed

Bell, A.J. and Sejnowski, T.J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159.10.1162/neco.1995.7.6.1129Suche in Google Scholar PubMed

Benis, D., David, O., Lachaux, J.P., Seigneuret, E., Krack, P., Fraix, V., Chabardès, S., and Bastin, J. (2014). Subthalamic nucleus activity dissociates proactive and reactive inhibition in patients with Parkinson’s disease. Neuroimage 91, 273–281.10.1016/j.neuroimage.2013.10.070Suche in Google Scholar PubMed

Boulinguez, P., Jaffard, M., Granjon, L., and Benraiss, A. (2008). Warning signals induce automatic EMG activations and proactive volitional inhibition: evidence from analysis of error distribution in simple RT. J. Neurophysiol. 99, 1572–1578.10.1152/jn.01198.2007Suche in Google Scholar PubMed

Boulinguez, P., Ballanger, B., Granjon, L., and Benraiss, A. (2009). The paradoxical effect of warning on reaction time: demonstrating proactive response inhibition with event-related potentials. Clin. Neurophysiol. 120, 730–737.10.1016/j.clinph.2009.02.167Suche in Google Scholar PubMed

Burle, B., Vidal, F., Tandonnet, C., and Hasbroucq, T. (2004). Physiological evidence for response inhibition in choice reaction time tasks. Brain Cognit. 56, 153–164.10.1016/j.bandc.2004.06.004Suche in Google Scholar PubMed

Buzsáki, G., Kaila, K., and Raichle, M. (2007). Inhibition and brain work. Neuron 56, 771–783.10.1016/j.neuron.2007.11.008Suche in Google Scholar PubMed PubMed Central

Casey, B.J., Castellanos, F.X., Giedd, J.N., Marsh, W.L., Hamburger, S.D., Schubert, A.B., Vauss, Y.C., Vaituzis, A.C., Dickstein, D.P., Sarfatti, S.E., et al. (1997). Implication of right frontostriatal circuitry in response inhibition and attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 36, 374–383.10.1097/00004583-199703000-00016Suche in Google Scholar PubMed

Chamberlain, S.R. and Sahakian, B.J. (2007). The neuropsychiatry of impulsivity. Curr. Opin. Psychiatry 20, 255–261.10.1097/YCO.0b013e3280ba4989Suche in Google Scholar PubMed

Chamberlain, S.R., Blackwell, A.D., Fineberg, N.A., Robbins, T.W., and Sahakian, B.J. (2005). The neuropsychology of obsessive compulsive disorder: the importance of failures in cognitive and behavioural inhibition as candidate endophenotypic markers. Neurosci. Biobehav. Rev. 29, 399–419.10.1016/j.neubiorev.2004.11.006Suche in Google Scholar PubMed

Chambers, C.D., Garavan, H., and Bellgrove, M.A. (2009). Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci. Biobehav. Rev. 33, 631–646.10.1016/j.neubiorev.2008.08.016Suche in Google Scholar PubMed

Chiu, Y.C. and Aron, A.R. (2013). Unconsciously triggered response inhibition requires an executive setting. J. Exp. Psychol. Gen. 143, 56–61.10.1037/a0031497Suche in Google Scholar PubMed PubMed Central

Cohen, M.X. and Cavanagh, J.F. (2011). Single-trial regression elucidates the role of prefrontal θ oscillations in response conflict. Front. Psychol. 28, 2–30.10.3389/fpsyg.2011.00030Suche in Google Scholar PubMed PubMed Central

Congedo, M., Gouy-Pailler, C., and Jutten, C. (2008). On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin. Neurophysiol. 119, 2677–2686.10.1016/j.clinph.2008.09.007Suche in Google Scholar PubMed

Congedo, M., John, R.E., De Ridder, D., and Prichep, L. (2010a). Group independent component analysis of resting state EEG in large normative samples. Int. J. Psychophysiol. 78, 89–99.10.1016/j.ijpsycho.2010.06.003Suche in Google Scholar PubMed

Congedo, M., John, R.E., De Ridder, D., Prichep, L., and Isenhart, R. (2010b). On the “dependence” of “independent” group EEG sources; an EEG study on two large databases. Brain Topogr. 23, 134–138.10.1007/s10548-009-0113-6Suche in Google Scholar PubMed

Criaud, M. and Boulinguez, P. (2013). Have we been asking the right questions when assessing response inhibition in go/no-go tasks with fMRI? A meta-analysis and critical review. Neurosci. Biobehav. Rev. 37, 11–23.10.1016/j.neubiorev.2012.11.003Suche in Google Scholar PubMed

Criaud, M., Wardak, C., Ben Hamed, S., Ballanger, B., and Boulinguez, P. (2012). Proactive inhibitory control of response as the default state of executive control. Front. Psychol. 3, 59.10.3389/fpsyg.2012.00059Suche in Google Scholar PubMed PubMed Central

Dalley, J.W., Everitt, B.J., and Robbins, T.W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron 69, 680–694.10.1016/j.neuron.2011.01.020Suche in Google Scholar PubMed

De Blasio, F.M. and Barry, R.J. (2013). Prestimulus δ and θ determinants of ERP responses in the go/nogo task. Int. J. Psychophysiol. 87, 279–288.10.1016/j.ijpsycho.2012.09.016Suche in Google Scholar PubMed

De Jong, R., Coles, M.G., Logan, G.D., and Gratton, G. (1990). In search of the point of no return: the control of response processes. J. Exp. Psychol. Hum. Percept. Perform. 16, 164–182.10.1037/0096-1523.16.1.164Suche in Google Scholar PubMed

Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., von Cramon, D.Y., and Engel, A.K. (2005). Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. J. Neurosci. 25, 11730–11737.10.1523/JNEUROSCI.3286-05.2005Suche in Google Scholar

Delorme, A. and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21.10.1016/j.jneumeth.2003.10.009Suche in Google Scholar

Delorme, A., Westerfield, M., and Makeig, S. (2007a). Medial prefrontal θ bursts precede rapid motor responses during visual selective attention. J. Neurosci. 27, 11949–11959.10.1523/JNEUROSCI.3477-07.2007Suche in Google Scholar

Delorme, A., Sejnowski, T., and Makeig, S. (2007b). Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34, 1443–1449.10.1016/j.neuroimage.2006.11.004Suche in Google Scholar

Dirnberger, G. and Jahanshahi, M. (2013). Executive dysfunction in Parkinson’s disease: a review. J. Neuropsychol. 7, 193–224.10.1111/jnp.12028Suche in Google Scholar

Donkers, F.C. and Van Boxtel, G.J. (2004). The N2 in go/no-go tasks reflects conflict monitoring not response inhibition. Brain Cognit. 56, 165–176.10.1016/j.bandc.2004.04.005Suche in Google Scholar

Eichele, T., Rachakonda, S., Brakedal, B., Eikeland, R., and Calhoun, V.D. (2011). EEGIFT: group independent component analysis for event-related EEG data. Comput. Intell. Neurosci. 2011, 129365.10.1155/2011/129365Suche in Google Scholar

Eimer, M. (1995). Stimulus-response compatibility and automatic response activation: evidence from psychophysiological studies. J. Exp. Psychol. Hum. Percept. Perform. 21, 837–854.10.1037/0096-1523.21.4.837Suche in Google Scholar

Endo, H., Kizuka, T., Masuda, T., and Takeda, T. (1999). Automatic activation in the human primary motor cortex synchronized with movement preparation. Brain Res. Cognit. Brain Res. 8, 229–239.10.1016/S0926-6410(99)00024-5Suche in Google Scholar

Engel, A.K. and Fries, P. (2010). β-Band oscillations – signalling the status quo? Curr. Opin. Neurobiol. 20, 156–165.10.1016/j.conb.2010.02.015Suche in Google Scholar PubMed

Enriquez-Geppert, S., Konrad, C., Pantev, C., and Huster, R.J. (2010). Conflict and inhibition differentially affect the N200/P300 complex in a combined go/nogo and stop-signal task. Neuroimage 51, 877–887.10.1016/j.neuroimage.2010.02.043Suche in Google Scholar

Erika-Florence, M., Leech, R., and Hampshire, A. (2014). A functional network perspective on response inhibition and attentional control. Nat. Commun. 5, 4073.10.1038/ncomms5073Suche in Google Scholar

Falkenstein, M., Hoormann, J., and Hohnsbein, J. (1999). ERP components in go/nogo tasks and their relation to inhibition. Acta Psychol. (Amst.) 101, 267–291.10.1016/S0001-6918(99)00008-6Suche in Google Scholar

Falkenstein, M., Hoormann, J., and Hohnsbein, J. (2002). Inhibition-related ERP components: variation with modality, age, and time-on-task. J. Psychophysiol. 16, 167–175.10.1027//0269-8803.16.3.167Suche in Google Scholar

Favre, E., Ballanger, B., Thobois, S., Broussolle, E., and Boulinguez, P. (2013). Deep brain stimulation of the subthalamic nucleus, but not dopaminergic medication, improves proactive inhibitory control of movement initiation in Parkinson’s disease. Neurotherapy 10, 154–167.10.1007/s13311-012-0166-1Suche in Google Scholar PubMed PubMed Central

Fineberg, N.A., Chamberlain, S.R., Goudriaan, A.E., Stein, D. J., Vanderschuren, L.J., Gillan, C.M., Shekar, S., Gorwood, P.A., Voon, V., Morein-Zamir, S., et al. (2014). New developments in human neurocognition: clinical, genetic, and brain imaging correlates of impulsivity and compulsivity. CNS Spectr. 19, 69–89.10.1017/S1092852913000801Suche in Google Scholar PubMed PubMed Central

Forstmann, B.U., van den Wildenberg, W.P., and Ridderinkhof, K.R. (2008). Neural mechanisms, temporal dynamics, and individual differences in interference control. J. Cognit. Neurosci. 20, 1854–1865.10.1162/jocn.2008.20122Suche in Google Scholar PubMed

Frank, M.J. (2006). Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural Netw. 19, 1120–1136.10.1016/j.neunet.2006.03.006Suche in Google Scholar PubMed

Frank, M.J., Samanta, J., Moustafa, A.A., and Sherman, S.J. (2007). Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism. Science 318, 1309–1312.10.1126/science.1146157Suche in Google Scholar PubMed

Gaetz, W., Edgar, J.C., Wang, D.J., and Roberts, T.P. (2011). Relating MEG measured motor cortical oscillations to resting γ-aminobutyric acid (GABA) concentration. Neuroimage 55, 616–621.10.1016/j.neuroimage.2010.12.077Suche in Google Scholar PubMed PubMed Central

Garavan, H., Ross, T., Murphy, K., Roche, R., and Stein, E. (2002). Dissociable executive functions in the dynamic control of behavior: inhibition, error detection, and correction. Neuroimage 17, 1820–1829.10.1006/nimg.2002.1326Suche in Google Scholar PubMed

Gaspar, C.M., Rousselet, G.A., and Pernet, C.R. (2011). Reliability of ERP and single-trial analyses. Neuroimage 58, 620–629.10.1016/j.neuroimage.2011.06.052Suche in Google Scholar PubMed

Gonzalez-Rosa, J.J., Inuggi, A., Blasi, V., Cursi, M., Annovazzi, P., Comi, G., Falini, A., and Leocani, L. (2013). Response competition and response inhibition during different choice-discrimination tasks: evidence from ERP measured inside MRI scanner. Int. J. Psychophysiol. 89, 37–47.10.1016/j.ijpsycho.2013.04.021Suche in Google Scholar PubMed

Grech, R., Cassar, T., Muscat, J., Camilleri, K.P., Fabri, S.G., Zervakis, M., Xanthopoulos, P., Sakkalis, V., and Vanrumste, B. (2008). Review on solving the inverse problem in EEG source analysis. J. Neuroeng. Rehabil. 5, 25.10.1186/1743-0003-5-25Suche in Google Scholar PubMed PubMed Central

Greenblatt, R.E., Ossadtchi, A., and Pflieger, M.E. (2005). Local linear estimators for the bioelectromagnetic inverse problem. IEEE Trans. Signal. Process. 53, 3403–3412.10.1109/TSP.2005.853201Suche in Google Scholar

Grin-Yatsenko, V.A., Baas, I., Ponomarev, V.A., and Kropotov, J.D. (2010). Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clin. Neurophysiol. 121, 281–289.10.1016/j.clinph.2009.11.015Suche in Google Scholar PubMed

Gross, J., Kujala, J., Hämäläinen, M., Timmermann, L., Schnitzler, A., and Salmelin, R. (2001). Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc. Natl. Acad. Sci. USA 98, 694–699.10.1073/pnas.98.2.694Suche in Google Scholar PubMed PubMed Central

Haegens, S., Nácher, V., Luna, R., Romo, R., and Jensen, O. (2011). α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proc. Natl. Acad. Sci. USA 108, 19377–19382.10.1073/pnas.1117190108Suche in Google Scholar PubMed PubMed Central

Hämäläinen, M.S. and Ilmoniemi, R.J. (1984). Interpreting measured magnetic fields of the brain: estimates of current distributions. Department of Technical Physics, Helsinki University of Technology.Suche in Google Scholar

Hindriks, R. and Van Putten, M.J. (2013). Thalamo-cortical mechanisms underlying changes in amplitude and frequency of human α oscillations. Neuroimage 70, 150–163.10.1016/j.neuroimage.2012.12.018Suche in Google Scholar PubMed

Hofmann, W., Schmeichel, B.J., and Baddeley, A.D. (2012). Executive functions and self-regulation. Trends Cognit. Sci. 16, 174–180.10.1016/j.tics.2012.01.006Suche in Google Scholar PubMed

Huster, R.J., Eichele, T., Enriquez-Geppert, S., Wollbrink, A., Kugel, H., Konrad, C., and Pantev, C. (2011). Multimodal imaging of functional networks and event-related potentials in performance monitoring. Neuroimage 56, 1588–1597.10.1016/j.neuroimage.2011.03.039Suche in Google Scholar

Huster, R.J., Enriquez-Geppert, S., Lavallee, C.F., Falkenstein, M., and Herrmann, C.S. (2013). Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. Int. J. Psychophysiol. 87, 217–233.10.1016/j.ijpsycho.2012.08.001Suche in Google Scholar

Hyvärinen, A. and Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430.10.1016/S0893-6080(00)00026-5Suche in Google Scholar

Isoda, M. and Hikosaka, O. (2011). Cortico-basal ganglia mechanisms for overcoming innate, habitual and motivational behaviors. Eur. J. Neurosci. 33, 2058–2069.10.1111/j.1460-9568.2011.07698.xSuche in Google Scholar

Jaffard, M., Benraiss, A., Longcamp, M., Velay, J.L., and Boulinguez, P. (2007). Cueing method biases in visual detection studies. Brain Res. 1179, 106–118.10.1016/j.brainres.2007.08.032Suche in Google Scholar

Jaffard, M., Longcamp, M., Velay, J.L., Anton, J.L., Roth, M., Nazarian, B., and Boulinguez, P. (2008). Proactive inhibitory control of movement assessed by event-related fMRI. Neuroimage 42, 1196–1206.10.1016/j.neuroimage.2008.05.041Suche in Google Scholar

Jahanshahi, M., Obeso, I., Baunez, C., Alegre, M., and Krack, P. (2015). Parkinson’s disease, the subthalamic nucleus, inhibition, and impulsivity. Mov. Disord. 30, 128–140.10.1002/mds.26049Suche in Google Scholar

Jensen, O. and Mazaheri, A. (2010). Shaping functional architecture by oscillatory α activity: gating by inhibition. Front. Hum. Neurosci. 4, 186.10.3389/fnhum.2010.00186Suche in Google Scholar

Jensen, O., Goel, P., Kopell, N., Pohja, M., Hari, R., and Ermentrout, B. (2005). On the human sensorimotor-cortex β rhythm: sources and modeling. Neuroimage. 26, 347–355.10.1016/j.neuroimage.2005.02.008Suche in Google Scholar

Jodo, E. and Kayama, Y. (1992). Relation of a negative ERP component to response-inhibition in a go/no-go task. Electroencephalogr. Clin. Neurophysiol. 82, 477–482.10.1016/0013-4694(92)90054-LSuche in Google Scholar

Jutten, C. and Herault, J. (1991). Blind separation of sources. Part I: an adaptive algorithm based on neuromimetic architecture. Signal Process. 24, 1–10.10.1016/0165-1684(91)90079-XSuche in Google Scholar

Kilavik, B.E., Zaepffel, M., Brovelli, A., MacKay, W.A., and Riehle, A. (2013). The ups and downs of β oscillations in sensorimotor cortex. Exp. Neurol. 245, 15–26.10.1016/j.expneurol.2012.09.014Suche in Google Scholar

Klimesch, W. (2012). α-Band oscillations, attention, and controlled access to stored information. Trends Cognit. Sci. 16, 606–617.10.1016/j.tics.2012.10.007Suche in Google Scholar

Klimesch, W., Sauseng, P., and Hanslmayr, S. (2007). EEG α oscillations: the inhibition-timing hypothesis. Brain Res. Rev. 53, 63–88.10.1016/j.brainresrev.2006.06.003Suche in Google Scholar

Knuth, K.H., Shah, A.S., Truccolo, W.A., Ding, M., Bressler, S.L., and Schroeder, C.E. (2006). Differentially variable component analysis: identifying multiple evoked components using trial-to-trial variability. J. Neurophysiol. 95, 3257–3276.10.1152/jn.00663.2005Suche in Google Scholar

Kopp, B., Mattler, U., Goertz, R., and Rist, F. (1996). N2,P3 and the lateralized readiness potential in a nogo task involving selective response priming. Electroencephalogr. Clin. Neurophysiol. 99, 19–27.10.1016/0921-884X(96)95617-9Suche in Google Scholar

Kornblum, S., Hasbroucq, T., and Osman, A. (1990). Dimensional overlap: cognitive basis for stimulus-response compatibility: a model and taxonomy. Psychol. Rev. 97, 253–270.10.1037/0033-295X.97.2.253Suche in Google Scholar PubMed

Kovacevic, N. and McIntosh, A.R. (2007). Groupwise independent component decomposition of EEG data and partial least square analysis. Neuroimage 35, 1103–1112.10.1016/j.neuroimage.2007.01.016Suche in Google Scholar PubMed

Kropotov, J.D. and Ponomarev, V.A. (2009). Decomposing N2 NOGO wave of event-related potentials into independent components. NeuroReport 20, 1592–1596.10.1097/WNR.0b013e3283309cbdSuche in Google Scholar PubMed

Kropotov, J.D., Ponomarev, V.A., Hollup, S., and Mueller, A. (2011). Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO/NOGO task. Neuroimage 57, 565–575.10.1016/j.neuroimage.2011.04.060Suche in Google Scholar PubMed

Krusienski, D.J., McFarland, D.J., and Wolpaw, J.R. (2012). Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. Brain Res. Bull. 87, 130–134.10.1016/j.brainresbull.2011.09.019Suche in Google Scholar

Lio, G. and Boulinguez, P. (2013). Greater robustness of second order statistics than higher order statistics algorithms to distortions of the mixing matrix in blind source separation of human EEG: implications for single-subject and group analyses. Neuroimage 67C, 137–152.10.1016/j.neuroimage.2012.11.015Suche in Google Scholar

Logothetis, N.K. (2008). What we can do and what we cannot do with fMRI. Nature 453, 869–878.10.1038/nature06976Suche in Google Scholar

Lopes da Silva, F. (2004). Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. Magn. Reson. Imaging 22, 1533–1538.10.1016/j.mri.2004.10.010Suche in Google Scholar

Lorincz, M.L., Kékesi, K.A., Juhász, G., Crunelli, V., and Hughes, S.W. (2009). Temporal framing of thalamic relay-mode firing by phasic inhibition during the a rhythm. Neuron 63, 683–696.10.1016/j.neuron.2009.08.012Suche in Google Scholar

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, R1–R13.10.1088/1741-2560/4/2/R01Suche in Google Scholar

Makeig, S. (2002). Response: event-related brain dynamics – unifying brain electrophysiology. Trends Neurosci. 25, 390.10.1016/S0166-2236(02)02198-7Suche in Google Scholar

Makeig, S. and Onton, J. (2011). Oxford Handbook of Event-Related Potential Components (New York, NY: Oxford University Press).Suche in Google Scholar

Makeig, S., Bell, A.J., Jung, T.P., and Sejnowski, T.J. (1996a). Independent component analysis of electroencephalographic data. Adv. Neural Inf. Process. Syst. 8, 145–151.Suche in Google Scholar

Makeig S., Jung T.P., Bell A.J., Ghahremani D., and Sejnowski T.J. (1996b). Blind separation of event-related brain response components. Psychophysiol. 33, 58.10.1037/e526132012-219Suche in Google Scholar

Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D., and Sejnowski, T.J. (1997). Blind separation of auditory event-related brain responses into independent components. Proc. Natl. Acad. Sci. USA 94, 10979–10984.10.1073/pnas.94.20.10979Suche in Google Scholar PubMed PubMed Central

Makeig, S., Debener, S., Onton, J., and Delorme, A. (2004). Mining event-related brain dynamics. Trends Cognit. Sci. 8, 204–210.10.1016/j.tics.2004.03.008Suche in Google Scholar PubMed

Makeig, S., Onton, J. (2009). ERP features and EEG dynamics: an ICA perspective. In: Oxford Handbook of Event-Related Potential Components (New York: Oxford).Suche in Google Scholar

Mathewson, K.E., Lleras, A., Beck, D.M., Fabiani, M., Ro, T., and Gratton, G. (2011). Pulsed out of awareness: EEG α oscillations represent a pulsed-inhibition of ongoing cortical processing. Front. Psychol. 2, 99.10.3389/fpsyg.2011.00099Suche in Google Scholar PubMed PubMed Central

Mazaheri, A., Nieuwenhuis, I.L., Van Dijk, H., and Jensen, O. (2009). Prestimulus α and μ activity predicts failure to inhibit motor responses. Hum. Brain Mapp. 30, 1794–1800.10.1002/hbm.20763Suche in Google Scholar PubMed PubMed Central

McBride, J., Boy, F., Husain, M., and Sumner, P. (2012). Automatic motor activation in the executive control of action. Front. Hum. Neurosci. 6, 82.10.3389/fnhum.2012.00082Suche in Google Scholar PubMed PubMed Central

Mele, S., Savazzi, S., Marzi, C.A., and Berlucchi, G. (2008). Reaction time inhibition from subliminal cues: is it related to inhibition of return? Neuropsychologia 46, 810–819.10.1016/j.neuropsychologia.2007.11.003Suche in Google Scholar PubMed

Michel, C. and He, B. (2011). EEG Mapping and Source Imaging. In: Niedermeyer’s Electroencephalography. 6th ed. Chapter 55. D. Schomer, and F. Lopes da Silva, eds. (Lippincott Williams and Wilkins), pp. 1179–1202.Suche in Google Scholar

Michel, C.M. and Murray, M.M. (2012). Towards the utilization of EEG as a brain imaging tool. Neuroimage 61, 371–385.10.1016/j.neuroimage.2011.12.039Suche in Google Scholar PubMed

Michel, C.M., Murray, M.M., Lantz, G., Gonzalez, S., Spinelli, L., and Grave de Peralta, R. (2004). EEG source imaging. Clin. Neurophysiol. 115, 2195–2222.10.1016/j.clinph.2004.06.001Suche in Google Scholar PubMed

Minelli, A., Marzi, C.A., and Girelli, M. (2007). Lateralized readiness potential elicited by undetected visual stimuli. Exp. Brain Res. 179, 683–690.10.1007/s00221-006-0825-8Suche in Google Scholar PubMed

Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A., and Wager, T.D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cognit. Psychol. 41, 49–100.10.1006/cogp.1999.0734Suche in Google Scholar PubMed

Mosher, J.C., Lewis, P.S., and Leahy, R.M. (1992). Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Sign. Process. 39, 541–557.10.1109/10.141192Suche in Google Scholar PubMed

Muthuraman, M., Hellriegel, H., Hoogenboom, N., Anwar, A.R., Mideksa, K.G., Krause, H., Schnitzler, A., Deuschl, G., and Raethjen, J. (2014). Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements. PLoS One 9, e91441.10.1371/journal.pone.0091441Suche in Google Scholar PubMed PubMed Central

Niedermeyer, E. (2005). Abnormal EEG Patterns: Epileptic and Paroxysmal. In: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, pp. 255.Suche in Google Scholar

Nieuwenhuis, S., Yeung, N., van den Wildenberg, W., and Ridderinkhof, K.R. (2003). Electrophysiological correlates of anterior cingulate function in a go/no-go task: effects of response conflict and trial type frequency. Cognit. Affect. Behav. Neurosci. 3, 17–26.10.3758/CABN.3.1.17Suche in Google Scholar

Onton, J. and Makeig, S. (2006). Information-based modeling of event-related brain dynamics. Prog. Brain Res. 159, 99–120.10.1016/S0079-6123(06)59007-7Suche in Google Scholar

Onton, J., Westerfield, M., Townsend, J., and Makeig, S. (2006). Imaging human EEG dynamics using independent component analysis. Neurosci. Biobehav. Rev. 30, 808–822.10.1016/j.neubiorev.2006.06.007Suche in Google Scholar

Pascual-Marqui, R.D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24D, 5–12.Suche in Google Scholar

Pascual-Marqui, R.D. (2007). Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. ArXiv Preprint. ArXiv07103341.Suche in Google Scholar

Pascual-Marqui, R.D., Sekihara, K., Brandeis, D., and Michel, C.M. (2009). Imaging the Electrical Neuronal Generators of EEG/MEG (Cambridge, UK: Cambridge University Press).10.1017/CBO9780511596889.004Suche in Google Scholar

Pernet, C.R., Sajda, P., and Rousselet, G.A. (2011). Single-trial analyses: why bother? Front. Psychol. 2, 322.10.3389/fpsyg.2011.00322Suche in Google Scholar

Pfurtscheller, G. and Lopes da Silva, F.H. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857.10.1016/S1388-2457(99)00141-8Suche in Google Scholar

Ponomarev, V.A., Mueller, A., Candrian, G., Grin-Yatsenko, V.A., and Kropotov, J.D. (2014). Group independent component analysis (gICA) and current source density (CSD) in the study of EEG in ADHD adults. Clin. Neurophysiol. 125, 83–97.10.1016/j.clinph.2013.06.015Suche in Google Scholar PubMed

Ratcliff, R., Philiastides, M.G., and Sajda, P. (2009). Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proc. Natl. Acad. Sci. USA 106, 6539–6544.10.1073/pnas.0812589106Suche in Google Scholar PubMed PubMed Central

Ridderinkhof, K.R. (2002). Micro- and macro-adjustments of task set: activation and suppression in conflict tasks. Psychol. Res. 66, 312–323.10.1007/s00426-002-0104-7Suche in Google Scholar PubMed

Ridderinkhof, K.R. (2011). Neurocognitive mechanisms of action control: resisting the call of the sirens. WIREs Cognit. Sci. 2, 174–192.10.1002/wcs.99Suche in Google Scholar PubMed

Rousselet, G.A., Husk, J.S., Bennett, P.J., and Sekuler, A.B. (2007). Single-trial EEG dynamics of object and face visual processing. Neuroimage 36, 843–862.10.1016/j.neuroimage.2007.02.052Suche in Google Scholar PubMed

Rousselet, G.A., Gaspar, C.M., Wieczorek, K.P., and Pernet, C.R. (2011). Modeling single-trial ERP reveals modulation of bottom-up face visual processing by top-down task constraints (in some subjects). Front. Psychol. 2, 137.10.3389/fpsyg.2011.00137Suche in Google Scholar PubMed PubMed Central

Scherg, M. and Picton, T.W. (1990). Separation and identification of event-related potential components by brain electric source analysis. Electroencephalogr. Clin. Neurophysiol. 42(Suppl), 24–37.Suche in Google Scholar

Schmiedt-Fehr, C. and Basar-Eroglu, C. (2011). Event-related δ and θ brain oscillations reflect age-related changes in both a general and a specific neuronal inhibitory mechanism. Clin. Neurophysiol. 122, 1156–1167.10.1016/j.clinph.2010.10.045Suche in Google Scholar PubMed

Schnitzler, A., Gross, J., and Timmermann, L. (2000). Synchronised oscillations of the human sensorimotor cortex. Acta Neurobiol. Exp. (Warsaw) 60, 271–287.Suche in Google Scholar

Sebastian, A., Pohl, M.F., Klöppel, S., Feige, B., Lange, T., Stahl, C., Voss, A., Klauer, K.C., Lieb, K., and Tüscher, O. (2013). Disentangling common and specific neural subprocesses of response inhibition. Neuroimage 64, 601–615.10.1016/j.neuroimage.2012.09.020Suche in Google Scholar PubMed

Sekihara, K., Sahani, M., and Nagarajan, S.S. (2005). Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. Neuroimage 25, 1056–1067.10.1016/j.neuroimage.2004.11.051Suche in Google Scholar PubMed PubMed Central

Shah, A.S., Bressler, S.L., Knuth, K.H., Ding, M., Mehta, A.D., Ulbert, I., and Schroeder, C.E. (2004). Neural dynamics and the fundamental mechanisms of event-related brain potentials. Cereb. Cortex 14, 476–483.10.1093/cercor/bhh009Suche in Google Scholar PubMed

Siegel, M., Donner, T.H., and Engel, A.K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134.10.1038/nrn3137Suche in Google Scholar PubMed

Sumner, P. and Husain, M. (2008). At the edge of consciousness: automatic motor activation and voluntary control. Neuroscientist 14, 474–486.10.1177/1073858408314435Suche in Google Scholar PubMed

Sutherland, M.T. and Tang, A.C. (2006a). Blind source separation can recover systematically distributed neuronal sources from resting EEG. In: Proceedings of the Second International Symposium on Communications, Control, and Signal Processing (ISCCSP), Marrakech, Morocco.Suche in Google Scholar

Sutherland, M.T. and Tang, A.C. (2006b). Reliable detection of bilateral activation in human primary somatosensory cortex by unilateral median nerve stimulation. Neuroimage 33, 1042–1054.10.1016/j.neuroimage.2006.08.015Suche in Google Scholar PubMed

Swick, D. and Chatham, C.H. (2014). Ten years of inhibition revisited. Front. Hum. Neurosci. 8, 329.10.3389/fnhum.2014.00329Suche in Google Scholar PubMed PubMed Central

Swick, D., Ashley, V., and Turken, U. (2011). Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. Neuroimage 56, 1655–1665.10.1016/j.neuroimage.2011.02.070Suche in Google Scholar PubMed

Tang, A. (2010). Applications of second order blind identification to high-density EEG-based brain imaging: a review. In: Advances in Neural Networks. ISSN 2010, Part 2, Proceedings. L.Q. Zhang, B.L. Lu, and J. Kwok, eds. (Springer-Verlag, Berlin), pp. 368–377.10.1007/978-3-642-13318-3_46Suche in Google Scholar

Tang, A., Sutherland, M., and Wang, Y. (2006). Contrasting single-trial ERPs between experimental manipulations: improving differentiability by blind source separation. Neuroimage 29, 335–346.10.1016/j.neuroimage.2005.07.058Suche in Google Scholar

Tang, A.C., Sutherland, M.T., Sun, P., Zhang, Y., Nakazawa, M., Korzekwa, A., Yang, Z., and Ding, M. (2007). Top-down versus bottom-up processing in the human brain: distinct directional influences revealed by integrating SOBI and Granger causality. In: Independent Component Analysis and Signal Separation (Springer), pp. 802–809.10.1007/978-3-540-74494-8_100Suche in Google Scholar

Tang, A., Sutherland, M., and Yang, Z. (2010). Capturing “trial-to-trial” variations in human brain activity: from laboratory to real world in: Functional significance of neuronal variability. M.Z. Ding, and D. Glanzman, eds. (New York, NY, USA: Oxford University Press).Suche in Google Scholar

Tipper, S.P. (2001). Does negative priming reflect inhibitory mechanisms? A review and integration of conflicting views. Q. J. Exp. Psychol. A 54, 321–343.10.1080/713755969Suche in Google Scholar

Van Boxtel, G.J., van der Molen, M.W., Jennings, J.R., and Brunia, C.H. (2001). A psychophysiological analysis of inhibitory motor control in the stop-signal paradigm. Biol. Psychol. 58, 229–262.10.1016/S0301-0511(01)00117-XSuche in Google Scholar

Van Dijk, H., Van der Werf, J., Mazaheri, A., Medendorp, W.P., and Jensen, O. (2010). Modulations in oscillatory activity with amplitude asymmetry can produce cognitively relevant event-related responses. Proc. Natl. Acad. Sci. USA 107, 900–905.10.1073/pnas.0908821107Suche in Google Scholar PubMed PubMed Central

Van Veen, B.D., van Drongelen, W., Yuchtman, M., and Suzuki, A. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 44, 867–880.10.1109/10.623056Suche in Google Scholar PubMed

Vigário, R., Särelä, J., Jousmäki, V., Hämäläinen, M., and Oja, E. (2000). Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47, 589–593.10.1109/10.841330Suche in Google Scholar PubMed

Voon, V. (2014). Models of impulsivity with a focus on waiting impulsivity: translational potential for neuropsychiatric disorders. Curr. Addict. Rep. 1, 281–288.10.1007/s40429-014-0036-5Suche in Google Scholar PubMed PubMed Central

Wagner, M., Fuchs, M., and Kastner, J. (2004). Evaluation of sLORETA in the presence of noise and multiple sources. Brain Topogr. 16, 277–280.10.1023/B:BRAT.0000032865.58382.62Suche in Google Scholar

Wessel, J.R. and Aron, A.R. (2014). Inhibitory motor control based on complex stopping goals relies on the same brain network as simple stopping. Neuroimage 103, 225–234.10.1016/j.neuroimage.2014.09.048Suche in Google Scholar PubMed PubMed Central

Zhang, Y., Tang, A.C., and Zhou, X. (2014). Synchronized network activity as the origin of a P300 component in a facial attractiveness judgment task. Psychophysiology 51, 285–289.10.1111/psyp.12153Suche in Google Scholar PubMed

Received: 2014-11-13
Accepted: 2015-3-8
Published Online: 2015-4-25
Published in Print: 2015-8-1

©2015 by De Gruyter

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