Individualized pattern recognition for detecting mind wandering from EEG during live lectures

English version

Autoři: Kiret Dhindsa ^aff001; Anita Acai ^aff001; Natalie Wagner ^aff001; Dan Bosynak ^aff002; Stephen Kelly ^aff001; Mohit Bhandari ^aff001; Brad Petrisor ^aff001; Ranil R. Sonnadara ^aff001
Působiště autorů: Department of Surgery, McMaster University, Hamilton, Ontario, Canada ^aff001; Research and High-Performance Computing Support, McMaster University, Hamilton, Ontario, Canada ^aff002; Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada ^aff003; Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada ^aff004; LIVELab, McMaster University, Hamilton, Ontario, Canada ^aff005; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada ^aff006; Department of Surgery, University of Toronto, Toronto, Ontario, Canada ^aff007
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222276

Souhrn

Neural correlates of mind wandering

The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.

Mind wandering detection

To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80–83%.

Conclusions

Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.

Klíčová slova:

Biology and life sciences – Physical sciences – Research and analysis methods – Neuroscience – Cognitive science – Cognitive psychology – Psychology – Social sciences – Sociology – Computer and information sciences – Mathematics – Simulation and modeling – Medicine and health sciences – Physiology – Diagnostic medicine – Clinical medicine – Applied mathematics – Algorithms – Education – Imaging techniques – Cognition – Brain mapping – Functional magnetic resonance imaging – Neuroimaging – Diagnostic radiology – Magnetic resonance imaging – Radiology and imaging – Electrophysiology – Neurophysiology – Bioassays and physiological analysis – Electrophysiological techniques – Brain electrophysiology – Electroencephalography – Clinical neurophysiology – Neural networks – Machine learning algorithms – Artificial intelligence – Machine learning – Attention – Lectures

Zdroje

1. Smallwood J, Mrazek MD, Schooler JW. Medicine for the wandering mind: mind wandering in medical practice. Med Educ. 2011;45(11):1072–80. doi: 10.1111/j.1365-2923.2011.04074.x 21988623

2. Stawarczyk D, Majerus S, Maj M, Van der Linden M, D’Argembeau A. Mind-wandering: phenomenology and function as assessed with a novel experience sampling method. Acta Psychol (Amst). 2011;136(3):370–81. doi: 10.1016/j.actpsy.2011.01.002 21349473

3. Smallwood J, Schooler JW. The restless mind. Psychol Bull. 2006;132(6):946–58. doi: 10.1037/0033-2909.132.6.946 17073528

4. Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN. Wandering minds: the default network and stimulus-independent thought. Science. 2007;315(5810):393–5. doi: 10.1126/science.1131295 17234951

5. Killingsworth MA, Gilbert DT. A wandering mind is an unhappy mind. Science. 2010;330(6006):932. doi: 10.1126/science.1192439 21071660

6. Kucyi A. Just a thought: how mind-wandering is represented in dynamic brain connectivity. NeuroImage. 2018;180(Part B):505–14. doi: 10.1016/j.neuroimage.2017.07.001 28684334

7. Christoff K, Irving ZC, Fox KCR, Spreng N, Andrews-Hanna JR. Mind-wandering as spontaneous thought: a dynamic framework. Nat Rev Neurosci. 2016;17(11):718–31. doi: 10.1038/nrn.2016.113 27654862

8. Mooneyham BW, Schooler JW. The costs and benefits of mind-wandering: a review. Can J Exp Psychol. 2013;67(1):11–8. doi: 10.1037/a0031569 23458547

9. Smallwood J, Schooler JW. The science of mind wandering: empirically navigating the stream of consciousness. Annu Rev Psychol. 2015;66:487–518. doi: 10.1146/annurev-psych-010814-015331 25293689

10. Smallwood J, O’Connor RC, Sudberry MV, Haskell C, Ballantyne C. The consequences of encoding information on the maintenance of internally generated images and thoughts: the role of meaning complexes. Conscious Cogn. 2004;13(4):789–820. doi: 10.1016/j.concog.2004.07.004 15522632

11. Smallwood J, McSpadden M, Schooler JW. The lights are on but no one’s home: meta-awareness and the decoupling of attention when the mind wanders. Psychon Bull Rev. 2007;14(3):527–33. doi: 10.3758/BF03194102 17874601

12. Smallwood J, McSpadden M, Luus B, Schooler JW. Segmenting the stream of consciousness: the psychological correlates of temporal structures in the time series data of a continuous performance task. Brain Cogn. 2008;66(1):50–6. doi: 10.1016/j.bandc.2007.05.004 17614178

13. Smallwood J, McSpadden M, Schooler JW. When attention matters: the curious incident of the wandering mind. M&C. 2008;36(6):1144–50. doi: 10.3758/MC.36.6.1144 18927032

14. Risko EF, Anderson N, Sarwal A, Engelhardt M, Kingstone A. Everyday attention: variation in mind wandering and memory in a lecture. Appl Cogn Psychol. 2012;26(2):234–42. doi: 10.1002/acp.1814

15. Szpunar KK, Jing HG, Schacter DL. Overcoming overconfidence in learning from video-recorded lectures: implications of interpolated testing for online education. J Appl Res Mem Cogn. 2014;3(3):161–4. doi: 10.1016/j.jarmac.2014.02.001

16. Wammes JD, Seli P, Cheyne JA, Boucher P, Smilek D. Mind wandering during lectures II: relation to academic performance. Scholars Teach Learn Psychol. 2016;2 (1):33–48. doi: 10.1037/stl0000055

17. Pachai AA, Acai A, LoGuidice AB, Kim JA. The mind that wanders: challenges and potential benefits of mind wandering in education. Scholars Teach Learn Psychol. 2016;2(2):134–46. doi: 10.1037/stl0000060

18. Wilson K, Korn JH. Attention during lectures: beyond ten minutes. Teach Psychol. 2007;24(2):85–9. doi: 10.1080/00986280701291291

19. Szpunar KK, Moulton ST, Schacter DL. Mind wandering and education: from the classroom to online learning. Front Psychol. 2013;4:495. doi: 10.3389/fpsyg.2013.00495 23914183

20. Lindquist SI, McLean JP. Daydreaming and its correlates in an educational environment. Learn Individ Differ. 2011;21(2):158–67. doi: 10.1016/j.lindif.2010.12.006

21. Jin CY, Borst JP, van Vugt MK. Predicting task-general mind-wandering with EEG. Cogn Affect Behav Neurosci. 2019. doi: 10.3758/s13415-019-00707-1 30850931

22. Barron E, Riby LM, Greer J, Smallwood J. Absorbed in thought: the effect of mind wandering on the processing of relevant and irrelevant events. Psychol Sci. 2011;22(5):596–601. doi: 10.1177/0956797611404083 21460338

23. McVay JC, Kane MJ. Conducting the train of thought: working memory capacity, goal neglect, and mind wandering in an executive-control task. J Exp Psychol Learn Mem Cogn. 2009;35(1):196–204. doi: 10.1037/a0014104 19210090

24. Schooler JW, Smallwood J, Christoff K, Handy TC, Reichle ED, Sayette MA. Meta-awareness, perceptual decoupling and the wandering mind. Trends Cogn Sci. 2011;15(7):P319–P26. doi: 10.1016/j.tics.2011.05.006 21684189

25. Weinstein Y., Mind-wandering how do I measure thee with probes? Let me count the ways. Behav Res Methods. 2018;50(2):642–61. doi: 10.3758/s13428-017-0891-9 28643155

26. Christoff K, Gordon AM, Smallwood J, Smith R, Schooler JW. Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc Natl Acad Sci U S A. 2009;106(21):8719–24. doi: 10.1073/pnas.0900234106 19433790

27. Antrobus JS. Information theory and stimulus-independent thought. Br J Psychol. 1968;59(4):423–39. doi: 10.1111/j.2044-8295.1968.tb01157.x

28. Seli P, Carriere JS, Smilek D. How few and far between? Examining the effects of probe rate on self-reported mind wandering. Front Psychol. 2013;4:430. doi: 10.3389/fpsyg.2013.00430 23882239

29. Spreng RN, Mar RA, Kim ASN. The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. J Cogn Neurosci. 2009;21(3):489–510. doi: 10.1162/jocn.2008.21029 18510452

30. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124(1):1–38. doi: 10.1196/annals.1440.011 18400922

31. Andrews-Hanna JR. The brain’s default network and its adaptive role in internal mentation. Neuroscientist. 2013;18(3):251–70. doi: 10.1177/1073858411403316 21677128

32. Dixon ML, Andrews-Hanna JR, Spreng RN, Irving ZC, Mills C, Girn M, et al. Interactions between the default network and dorsal attention network vary across default subsystems, time, and cognitive states. NeuroImage. 2017;147:632–49. doi: 10.1016/j.neuroimage.2016.12.073 28040543

33. McVay JC, Kane MJ. Does mind wandering reflect executive function or executive failure? Comment on Smallwood and Schooler (2006) and Watkins (2008). Psychol Bull. 2010;136(2):188–207. doi: 10.1037/a0018298 20192557

34. Kane MJ, McVay JC. What mind wandering reveals about executive-control abilities and failures. Curr Dir Psychol Sci. 2012;21(5):348–54. doi: 10.1177/0963721412454875

35. Axelrod V, Rees G, Lavidor M, Bar M. Increasing propensity to mind-wander with transcranial direct current stimulation. Proc Natl Acad Sci U S A. 2015;112(11):3314–9. doi: 10.1073/pnas.1421435112 25691738

36. Smallwood J, Beach E, Schooler JW, Handy TC. Going AWOL in the brain: mind wandering reduces cortical analysis of external events. J Cogn Neurosci. 2008;20(3):458–69. doi: 10.1162/jocn.2008.20037 18004943

37. Smallwood J. Distinguishing how from why the mind wanders: a process-occurrence framework for self-generated mental activity. Psychol Bull. 2013;139(3):519–35. doi: 10.1037/a0030010 23607430

38. Girn M, Mills C, Laycock E, Ellamil M, Ward L, Christoff K. Neural dynamics of spontaneous thought: an electroencephalographic study. Proceedings of the 11th International Conference on Augmented Cognition, Lecture Notes in Computer Science, vol 10284; 2017; Vancouver, British Columbia, Canada. Cham, Switzerland: Springer. p. 28–44.

39. Murray MM, Brunet D, Michel CM. Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr. 2008;20(4):249–64. doi: 10.1007/s10548-008-0054-5 18347966

40. Braboszcz C, Delorme A. Lost in thoughts: neural markers of low alertness during mind wandering. NeuroImage. 2011;54(4):3040–7. doi: 10.1016/j.neuroimage.2010.10.008 20946963

41. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29(2–3):169–95. doi: 10.1016/S0165-0173(98)00056-3 10209231

42. Herrmann CS, Strüber D, Helfrich RF, Engel AK. EEG oscillations: from correlation to causality. Int J Psychophysiol. 2016;103:12–21. doi: 10.1016/j.ijpsycho.2015.02.003 25659527

43. Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherencez. Trends Cogn Sci. 2005;9(10):474–80. doi: 10.1016/j.tics.2005.08.011 16150631

44. Fell J, Axmacher N. The role of phase synchronization in memory processes. Nat Rev Neurosci. 2011;12(2):105–18. doi: 10.1038/nrn2979 21248789

45. Schnitzler A, Gross J. Normal and pathological oscillatory communication in the brain. Nat Rev Neurosci. 2005;6(4):285–96. doi: 10.1038/nrn1650 15803160

46. Engel AK, Singer W. Temporal binding and the neural correlates of sensory awareness. Trends Cogn Sci. 2001;5(1):16–25. doi: 10.1016/S1364-6613(00)01568-0 11164732

47. Varela F, Lachaux J-P, Rodriguez E, Martinerie J. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci. 2001;2(4):229–39. doi: 10.1038/35067550 11283746

48. Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol. 2003;114(2):171–83. doi: 10.1016/S1388-2457(02)00362-0 12559224

49. Keune PM, Hansen S, Weber E, Zapf F, Habich J, Muenssinger J, et al. Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis. Clin Neurophysiol. 2017;128(9):1746–54. doi: 10.1016/j.clinph.2017.06.253 28772244

50. van Son D, De Blasio FM, Fogarty JS, Angelidis A, Barry RJ, Putman P. Frontal EEG theta/beta ratio during mind wandering episodes. Biol Psychol. 2019;140:19–27. doi: 10.1016/j.biopsycho.2018.11.003 30458199

51. Boudewyn MA, Long DL, Traxler MJ, Lesh TA, Dave S, Mangun GR, et al. Sensitivity to referential ambiguity in discourse: the role of attention, working memory, and verbal ability. J Cogn Neurosci. 2015;27(12):2309–23. doi: 10.1162/jocn_a_00837 26401815

52. Boudewyn MA, Carter CS. I must have missed that: alpha-band oscillations track attention to spoken language. Neuropsychologia. 2018;117:148–55. doi: 10.1016/j.neuropsychologia.2018.05.024 29842859

53. Smilek D, Carriere JSA, Cheyne JA. Out of mind, out of sight: eye blinking as indicator and embodient of mind wandering. Psychol Sci. 2010;21(6):786–9. doi: 10.1177/0956797610368063 20554601

54. Hutt S, Mills C, Bosch N, Krasich K, Brockmole J, D’Mello S. Out of the fr-“eye-ing pan: towards gaze-based models of attention during learning with technology in the classroom. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization; 2017 Jul; Bratislava, Slovakia. New York, NY: ACM. p. 94–103.

55. Faber M, Bixler R, D’Mello SK. An automated behavioral measure of mind wandering during computerized reading. Behav Res Methods. 2018;50(1):134–50. doi: 10.3758/s13428-017-0857-y 28181186

56. Smallwood J, Davies JB, Heim D, Finnigan F, Sudberry M, O’Connor R, et al. Subjective experience and the attentional lapse: task engagement and disengagement during sustained attention. Conscious Cogn. 2004;13(4):657–90. doi: 10.1016/j.concog.2004.06.003 15522626

57. Pham P, Wang J. AttentiveLearner: improving mobile MOOC learning via implicit heart rate tracking. In: Conati C, Heffernan N, Mitrovic A, Verdejo MF, editors. Proceedings of the 17th International Conference on Artificial Intelligence in Education, Lecture Notes in Computer Science, vol 9112; 2015 Jun; Madrid, Spain. Cham, Switzerland: Springer. p. 367–76.

58. Blanchard N, Bixler R, Joyce T, D’Mello S. Automated physiological-based detection of mind wandering during learning. In: Trausan-Matu S, Boyer KE, Crosby M, Panourgia K, editors. Proceedings of the 12th International Conference on Intelligent Tutoring Systems, Lecture Notes in Computer Science, vol 8474; 2014 Jun; Honolulu, Hawaii, United States. Cham, Switzerland: Springer. p. 55–60.

59. Mittner M, Boekel W, Tucker AM, Turner BM, Heathcote A, Fostmann BU. When the brain takes a break: A model-based analysis of mind wandering. J Neurosci. 2014;34(49):16286–95. doi: 10.1523/JNEUROSCI.2062-14.2014 25471568

60. Durantin G, Dehais F, Delorme A. Characterization of mind wandering using fNIRS. Front Syst Neurosci. 2015;9(45). doi: 10.3389/fnsys.2015.00045 25859190

61. Zheng W-L, Lu B-L. Personalizing EEG-based affective models with transfer learning. Proceedings of the 25th International Joint Conference on Artificial Intelligence; 2016; New York, New York, USA: AAAI Press. p. 2732–8.

62. Dhindsa K, Becker S. Emotional reaction recognition from EEG. Proceedings of the 7th International Workshop on Pattern Recognition in Neuroimaging; 2017; Toronto, Ontario, Canada: IEEE. p. 1–4.

63. Wang J-W, Nie D, Lu B-L. Emotional state classification from EEG data using machine learning approach. Neurocomputing. 2014;129:94–106. doi: 10.1016/j.neucom.2013.06.046

64. Boshra R, Dhindsa K, Boursalie O, Ruiter KI, Sonnadara RR, Samavi R, et al. From group-level statistics to single-subject prediction: machine learning detection of concussion in retired athletes. IEEE Trans Neural Syst Rehabil Eng. 2019;27(7):1492–501. doi: 10.1109/TNSRE.2019.2922553 31199262

65. Jayaram V, Alamgir M, Altun Y, Schölkopf B, Grosse-Wentrup M. Transfer learning in brain-computer interfaces. IEEE Computational Intelligence Magazine. 2016;11(1):20–31. doi: 10.1109/MCI.2015.2501545

66. Meltzer JA, Negishi M, Mayes LC, Constable RT. Individual differences in EEG theta and alpha dynamics during working memory correlate with fMRI responses across subjects. Clin Neurophysiol. 2007;118(11):2419–36. doi: 10.1016/j.clinph.2007.07.023 17900976

67. Kane MJ, Smeekens BA, von Bastian CC, Lurquin JH, Carruth NP, Miyake A. A combined experimental and individual-differences investigation into mind wandering during a video lecture. J Exp Psychol Gen. 2017;146(11):1649–74. doi: 10.1037/xge0000362 29094964

68. Robison MK, Gath KI, Unsworth N. The neurotic wandering mind: an individual differences investigation of neuroticism, mind-wandering, and executive control. Q J Exp Physiol. 2017;70(4):649–63. doi: 10.1080/17470218.2016.1145706 26821933

69. Smeekens BA, Kane MJ. Working memory capacity, mind wandering, and creative cognition: an individual-differences investigation into the benefits of controlled versus spontaneous thought. Psychol Aesthet Crea. 2016;10(4):389–415. doi: 10.1037/aca0000046 28458764

70. Kucyi A, Davis KD. Dynamic functional connectivity of the default mode network tracks daydreaming. NeuroImage. 2014;100(15):471–80. doi: 10.1016/j.neuroimage.2014.06.044 24973603

71. Chou Y-h, Sundman M, Whitson HE, Gaur P, Chu M-L, Weingarten CP, et al. Maintenance and representation of mind wandering during resting-state fMRI. Sci Rep. 2017;7:40722. doi: 10.1038/srep40722 28079189

72. McMaster LIVELab. n.d. Available from: http://livelab.mcmaster.ca/.

73. Sana F, Weston T, Cepeda NJ. Laptop multitasking hinders classroom learning for both users and nearby peers. Comput Educ. 2013;62:24–31. doi: 10.1016/j.compedu.2012.10.003

74. Maxwell SE, Delaney HD. Designing experiments and analyzing data. 2nd ed. New York: Taylor & Francis; 2004.

75. Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. MEG and EEG data analysis with MNE-Python. Front Neurosci. 2013;7:267. doi: 10.3389/fnins.2013.00267 24431986

76. Carriere JS, Seli P, Smilek D. Wandering in both mind and body: individual differences in mind wandering and inattention predict fidgeting. Can J Exp Psychol. 2013;67(1):19–31. doi: 10.1037/a0031438 23458548

77. Chou Y-L. Statistical analysis with business and economic applications. 2nd. ed. New York: Holt, Rinehart, and Winston; 1975.

78. Jas M, Engemann DA, Bekhti Y, Raimondo F, Gramfort A. Autoreject: automated artifact rejection for MEG and EEG data. NeuroImage. 2017;159:417–29. doi: 10.1016/j.neuroimage.2017.06.030 28645840

79. Urigüen JA, Garcia-Zapirain B. EEG artifact removal—state-of-the-art and guidelines. J Neural Eng. 2015;12(3). doi: 10.1088/1741-2560/12/3/031001 25834104

80. Lee T-W, Girolami M, Sejnowski TJ. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 1999;11(2):417–41. doi: 10.1162/089976699300016719 9950738

81. Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage. 2007;34(4):1443–9. doi: 10.1016/j.neuroimage.2006.11.004 17188898

82. Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000;8(4):441–6. 11204034

83. Blankertz B, Tomioka R, Lemm S. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag. 2008;25(1):41–56.

84. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97. doi: 10.1007/BF00994018

85. Dhindsa K, Becker S. Toward an open-ended BCI: a user-centered coadaptive design. Neural Comput. 2017;29(10):2742–68. doi: 10.1162/neco_a_01001 28777722

86. Dhindsa K, Carcone D, Becker S. A brain-computer interface based on abstract visual and auditory imagery: evidence for an effect of artistic training. 11th International Conference on Augmented Cognition, AC 2017, Held as Part of HCI International 2017; Vancouver, BC, Canada: Springer.

87. Dietterich TG. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998;10(7):1895–923. doi: 10.1162/089976698300017197 9744903

88. Wammes JD, Boucher P, Seli P, Smilek D. Mind wandering during lecture I: Changes in rates across an entire semester. Scholars Teach Learn Psychol. 2016;2(1):13–32. doi: 10.1037/stl0000053

89. Arvaneh M, Guan C, Ang KK, Quek C. Robust EEG channel selection across sessions in brain-computer interface involving stroke patients. Proceedings of the 2012 International Joint Conference on Neural Networks; 2012, Jun; Brisbane, Queensland, Australia: IEEE. p. 1–6.

90. Cho H, Ahn M, Kim K, Jun SC. Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition. J Neural Eng. 2015;12(6). doi: 10.1088/1741-2560/12/6/066009 26447843

91. Goel P, Joshi RB, Sur M, Murthy HA. A common spatial pattern approach for classification of mental counting and motor execution EEG. In: Tiwary US, editor. Proceedings of the 10th International Conference on Intelligent Human Computer Interaction, Lecture Notes in Computer Science, vol 11278; 2018, Dec; Allahabad, India. Cham, Switzerland: Springer. p. 26–35.

92. Sayed-Mouchaweh M, Lughofer E, editors. Learning in non-stationary environments: methods and applications. New York, NY: Springer; 2012.

93. Seli P, Ralph BCW, Konishi M, Smilek D, Schacter DL. What did you have in mind? Examining the content of intentional and unintentional types of mind wandering. Conscious Cogn. 2017;51:149–56. doi: 10.1016/j.concog.2017.03.007 28371688

94. Song J, Davey C, Poulsen C, Luu P, Turovets S, Anderson E, et al. EEG source localization: sensor density and head surface coverage. J Neurosci Methods. 2015;256:9–21. doi: 10.1016/j.jneumeth.2015.08.015 26300183

95. Sohrabpour A, Lu Y, Kankirawatana P, Blount J, Kim H, He B. Effect of EEG electrode number on epileptic source localization in pediatric patients. Clin Neurophysiol. 2015;126(3):472–80. doi: 10.1016/j.clinph.2014.05.038 25088733

96. Jann K, Kottlow M, Dierks T, Boesch C, Koenig T. Topographic electrophysiological signatures of fMRI resting state networks. PLOS ONE. 2010;5(9):e12945. doi: 10.1371/journal.pone.0012945 20877577

97. Knyazev GG. EEG correlates of self-referential processing. Front Hum Neurosci. 2013;6:264. doi: 10.3389/fnhum.2013.00264 23761757

98. Laufs H, Krakow K, Sterzer P, Eger E, Beyerle A, Salek-Haddadi A, et al. Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proc Natl Acad Sci U S A. 2003;100(19):11053–8. doi: 10.1073/pnas.1831638100 12958209

99. Gonçalves SI, de Munck JC, Pouwels PJW, Schoonhoven R, Kuijer JPA, Maurits NM, et al. Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: inter-subject variability. NeuroImage. 2006;30(1):203–13. doi: 10.1016/j.neuroimage.2005.09.062 16290018

100. Jann K, Dierks T, Boesch C, Kottlow M, Strik W, Koenig T. BOLD correlates of EEG alpha phase-locking and the fMRI default mode network. NeuroImage. 2009;45(3):903–16. doi: 10.1016/j.neuroimage.2009.01.001 19280706

101. Goldman R, Stern J, Engel J, Cohen M. Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport. 2002;13(18):2487–92. doi: 10.1097/01.wnr.0000047685.08940.d0 12499854

102. Baumeister J, Barthel T, Geiss KR, Weiss M. Influence of phosphatidylserine on cognitive performance and cortical activity after induced stress. Nutr Neurosci. 2008;11(3):103–10. doi: 10.1179/147683008X301478 18616866

103. Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng. 2018;15(3):031005. doi: 10.1088/1741-2552/aab2f2 29488902

104. Saha S, Ahmed KI, Mostafa R, Khandoker AH, Hadjileontiadis L. Enhanced inter-subject brain computer interface with associative sensorimotor oscillations. Healthc Technol Lett. 2017;4(1):39–43. doi: 10.1049/htl.2016.0073 28529762

105. Zedelius CM, Gross ME, Schooler JW. Mind wandering: more than a bad habit. In: Verplanken B, editor. The psychology of habit. Basel: Springer; 2018. p. 363–78.

106. Fox KCR, Spreng RN, Ellamil M, Andrews-Hanna JR, Christoff K. The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes. NeuroImage. 2015;111:611–21. doi: 10.1016/j.neuroimage.2015.02.039 25725466

107. Seli P, Carriere JS, Smilek D. Not all mind wandering is created equal: dissociating deliberate from spontaneous mind wandering. Psychol Res. 2015;79(5):750–8. doi: 10.1007/s00426-014-0617-x 25284016