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EEG in classroom: EMD features to detect situational interest of students during learning

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Abstract

Situational interest is widely explored in the psychology and education domains. It is proven to have positive effect on learning and academic achievement. Nonetheless, not much attention is given for assessing the feasibility of detecting this interest in natural classroom physiologically. Therefore, this study investigates the possibility of detecting situational interest using Electroencephalogram (EEG) in classroom. After preprocessing of EEG data, they were decomposed using Empirical Mode Decomposition (EMD). The resulted Intrinsic Mode Functions (IMFs) were ranked based on their significance using T-test and Receiver Operator Characteristics (ROC) in descending order. A matrix was constructed for all participants using the best six features from four EEG channels. These selected features were fed into Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers with 10 cross validation. While SVM achieved high accuracy of 93.3% and 87.5% for two data sets using features from the four EEG channels, KNN classifier achieved high accuracy of 87.5% and 86.7% in the same datasets using single EEG channel. It is found that gamma and delta bands can be used successfully to detect situational interest of students during learning in classrooms. Furthermore, data of single EEG channel - F3 in this study- was efficient to detect student’s situational interest in simultaneous recording of EEG in classroom.

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References

  1. Ayvaz Ü, Yaman H, Mersin N, Yilmaz Y, Durmuş S (2017) The perspectives of primary mathematics teacher candidates about equal sign: the EEG case. Univ J Educ Res 5(12A):111–120

    Article  Google Scholar 

  2. Azcarraga J, Suarez MT (2013) Recognizing student emotions using brainwaves and mouse behavior data. Int J Distance Educ Technol 11(2):1–15. https://doi.org/10.4018/jdet.2013040101

    Article  Google Scholar 

  3. Baars BJ, Gage NM (2010) Chapter 5 - the brain. In: Cognition, brain, and consciousness, Second edn. Academic Press, London, pp 126–154. https://doi.org/10.1016/B978-0-12-375070-9.00005-X

    Chapter  Google Scholar 

  4. Babiker A, Faye I, Malik A Investigation of situational interest effects on learning using physiological sensors: Preliminary result. In: 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS), 15–17 Aug. 2016 2016. pp 1–5. https://doi.org/10.1109/ICIAS.2016.7824075

  5. Barbey AK, Koenigs M, Grafman J (2013) Dorsolateral prefrontal contributions to human working memory. Cortex; J Devot Study Nervous Syst Behav 49(5):1195–1205. https://doi.org/10.1016/j.cortex.2012.05.022

    Article  Google Scholar 

  6. Bauer EP, Paz R, Paré D (2007) Gamma oscillations coordinate Amygdalo-Rhinal interactions during learning. J Neurosci 27(35):9369

    Article  Google Scholar 

  7. Beam W, Borckardt JJ, Reeves ST, George MS (2009) An efficient and accurate new method for locating the F3 position for prefrontal TMS applications. Brain Stimul 2(1):50–54. https://doi.org/10.1016/j.brs.2008.09.006

    Article  Google Scholar 

  8. Biju KS, Hakkim HA, Jebucumar MG (2017) Ictal EEG classification based on amplitude and frequency contours of IMFs. Biocybernet Biomed Eng 37(1):172–183. https://doi.org/10.1016/j.bbe.2016.12.005

    Article  Google Scholar 

  9. Börner D, Kalz M, Specht M (2014) Lead me gently: facilitating knowledge gain through attention-ware ambient learning displays. Comput Educ 78:10–19

    Article  Google Scholar 

  10. Cernea D, Olech P, Ebert A, Kerren A (2011) EEG-Based Measurement of Subjective Parameters in Evaluations. In: C. S (ed) HCI International 2011 - posters' Extended Abstracts. HCI 2011. Communciations in Computer and Information Sciences. Springer, Berlin, Heidlberg

  11. Chen C-M, Wang J-Y (2017) Effects of online synchronous instruction with an attention monitoring and alarm mechanism on sustained attention and learning performance. Interact Learn Environ 26(4):427–443. https://doi.org/10.1080/10494820.2017.1341938

    Article  Google Scholar 

  12. Essex BG, Clinton SA, Wonderley LR, Zald DH (2012) The impact of the posterior parietal and dorsolateral prefrontal cortices on the optimization of Long-term versus immediate value. J Neurosci 32(44):15403

    Article  Google Scholar 

  13. Fries P, Reynolds JH, Rorie AE, Desimone R (2001) Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291(5508):1560–1563. https://doi.org/10.1126/science.1055465

    Article  Google Scholar 

  14. Ghergulescu I, Muntean CH (2014) A novel sensor-based methodology for Learner's motivation analysis in game-based learning. Interact Comput 26(4):305–320

    Article  Google Scholar 

  15. Gullick MM, Wolford G (2014) Brain Systems involved in arithmetic with positive versus negative numbers. Hum Brain Mapp 33(2):539–551

    Article  Google Scholar 

  16. Güntekin B, Başar E (2016) Review of evoked and event-related delta responses in the human brain. Int J Psychophysiol 103:43–52. https://doi.org/10.1016/j.ijpsycho.2015.02.001

    Article  Google Scholar 

  17. Harackiewicz JM, Hulleman CS (2010) The importance of interest: the role of achievement goals and task values in promoting the development of interest. Soc Personal Psychol Compass 4(1):42–52. https://doi.org/10.1111/j.1751-9004.2009.00207.x

    Article  Google Scholar 

  18. Harmony T, Fernández T, Silva J, Bernal J, Díaz-Comas L, Reyes A, Marosi E, Rodríguez M, Rodríguez M (1996) EEG delta activity: an indicator of attention to internal processing during performance of mental tasks. Int J Psychophysiol 24(1):161–171. https://doi.org/10.1016/S0167-8760(96)00053-0

    Article  Google Scholar 

  19. Hidi S (1990) Interest and its contribution as a mental resource for learning. Rev Educ Res 60(4):549–571. https://doi.org/10.3102/00346543060004549

    Article  Google Scholar 

  20. Hidi S (2006) Interest: A unique motivational variable. Educ Res Rev 1(2):69–82. https://doi.org/10.1016/j.edurev.2006.09.001

    Article  Google Scholar 

  21. Hidi S, Renninger KA (2006) The four-phase model of interest development. Educ Psychol 41(2):111–127. https://doi.org/10.1207/s15326985ep4102_4

    Article  Google Scholar 

  22. Hogervorst MA, Brouwer A-M, van Erp JBF (2014) Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Front Neurosci 8:322

    Article  Google Scholar 

  23. Hsu C-H, Lee C-Y, Liang W-K (2016) An improved method for measuring mismatch negativity using ensemble empirical mode decomposition. J Neurosci Methods 264:78–85. https://doi.org/10.1016/j.jneumeth.2016.02.015

    Article  Google Scholar 

  24. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London Ser A: Math, Phys Eng Sci 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193

    Article  MathSciNet  MATH  Google Scholar 

  25. Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: the Hilbert spectrum. Annu Rev Fluid Mech 31:417–457

    Article  MathSciNet  Google Scholar 

  26. Jeneson A, Squire LR Working memory, long-term memory, and medial temporal lobe function. Learning & memory (Cold Spring Harbor, NY) 19 (1):15–25. https://doi.org/10.1101/lm.024018.111

  27. Jia X, Kohn A (2011) Gamma rhythms in the brain. PLoS Biol 9(4):e1001045. https://doi.org/10.1371/journal.pbio.1001045

    Article  Google Scholar 

  28. Jimenez-Molina A, Retamal C, Lira H (2018) Using psychophysiological sensors to assess mental workload during web browsing. Sensors (Basel) 18(2). https://doi.org/10.3390/s18020458

  29. Jones R (2001) Learning by the rules. Nat Rev Neurosci 2:760. https://doi.org/10.1038/35097503

    Article  Google Scholar 

  30. Kaiser J, Hertrich I, Ackermann H, Lutzenberger W (2006) Gamma-band activity over early sensory areas predicts detection of changes in audiovisual speech stimuli. NeuroImage 30(4):1376–1382. https://doi.org/10.1016/j.neuroimage.2005.10.042

    Article  Google Scholar 

  31. Kane MJ, Engle RW (2002) The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon Bull Rev 9(4):637–671. https://doi.org/10.3758/BF03196323

    Article  Google Scholar 

  32. Kounios J, Beeman M (2009) The Aha! Moment: the cognitive neuroscience of insight. Curr Dir Psychol Sci 18(4):210–216. https://doi.org/10.1111/j.1467-8721.2009.01638.x

    Article  Google Scholar 

  33. Kumfor F, Irish M, Hodges JR, Piguet O (2014) Frontal and temporal lobe contributions to emotional enhancement of memory in behavioral-variant frontotemporal dementia and Alzheimer's disease. Front Behav Neurosci 8(225). https://doi.org/10.3389/fnbeh.2014.00225

  34. Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816. https://doi.org/10.1016/j.compbiomed.2013.04.002

    Article  Google Scholar 

  35. Liang H, Bressler SL, Desimone R, Fries P (2005) Empirical mode decomposition: a method for analyzing neural data. Neurocomputing 65-66:801–807. https://doi.org/10.1016/j.neucom.2004.10.077

    Article  Google Scholar 

  36. Liu NH, Chiang CY, Chu HC (2013) Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors (Basel) 13(8):10273–10286. https://doi.org/10.3390/s130810273

    Article  Google Scholar 

  37. Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4(2):R1

    Article  Google Scholar 

  38. Matthews G, Warm JS, Smith AP (2017) Task engagement and attentional resources. Hum Factors J Hum Factors Ergon Soc 59:44–61

    Article  Google Scholar 

  39. Md Nor N, Bar AW, Shaikh Salleh SH (2013) Correlation of Student’s precursor emotion towards learning science interest using EEG. Telkomnika 13(4):1162–1169

    Article  Google Scholar 

  40. Mitchell M (1993) Situational interest: its multifaceted structure in the secondary school mathematics classroom. J Educ Psychol 85(3):424–436. https://doi.org/10.1037/0022-0663.85.3.424

    Article  Google Scholar 

  41. Moldovan AN, Ghergulescu I, Muntean CH (2017) Analysis of learner interest, QoE and EEG-based affective states in multimedia Mobile learning. IEEE 17th Int Conf Adv Learn Technol (ICALT) 2017:398–402. https://doi.org/10.1109/ICALT.2017.93

    Google Scholar 

  42. Myrden A, Chau (2017) A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING https://doi.org/10.1109/TNSRE.2016.2641956

  43. Okafor C, Egbon O (2011) Academic performance of male versus female accounting undergraduate students: evidence from Nigeria. Higher Educ Stud 1(1)

  44. Pesaran B, Pezaris JS, Sahani M, Mitra PP, Andersen RA (2002) Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat Neurosci 5:805. https://doi.org/10.1038/nn890 https://www.nature.com/articles/nn890#supplementary-information

    Article  Google Scholar 

  45. Peterson CK, Shackman AJ, Harmon-Jones E (2008) The role of asymmetrical frontal cortical activity in aggression. Psychophysiology 45(1):86–92

    Google Scholar 

  46. Poulsen AT, Kamronn S, Dmochowski J, Parra LC, Hansen LK (2017) EEG in the classroom: synchronised neural recordings during video presentation. Sci Rep 7:43916. https://doi.org/10.1038/srep43916

    Article  Google Scholar 

  47. Quitadamo LR, Cavrini F, Sbernini L, Riillo F, Bianchi L, Seri S, Saggio G (2017) Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review. J Neural Eng 14(1):011001

    Article  Google Scholar 

  48. Root JC, Wong PS, Kinsbourne M (2006) Left hemisphere specialization for response to positive emotional expressions: a divided output methodology. Emotion 6(3):473–483. https://doi.org/10.1037/1528-3542.6.3.473

    Article  Google Scholar 

  49. Rose M, Sommer T, Büchel C (2006) Integration of local features to a global percept by neural coupling. Cereb Cortex 16(10):1522–1528. https://doi.org/10.1093/cercor/bhj089

    Article  Google Scholar 

  50. Rotgans JI, Schmidt HG (2014) Situational interest and learning: thirst for knowledge. Learn Instr 32:37–50. https://doi.org/10.1016/j.learninstruc.2014.01.002

    Article  Google Scholar 

  51. Rotgans JI, Schmidt HG (2017) Interest development: arousing situational interest affects the growth trajectory of individual interest. Contemp Educ Psychol 49:175–184. https://doi.org/10.1016/j.cedpsych.2017.02.003

    Article  Google Scholar 

  52. Sandkühler S, Bhattacharya J (2008) Deconstructing insight: EEG correlates of insightful problem solving. PLoS One 3(1):e1459. https://doi.org/10.1371/journal.pone.0001459

    Article  Google Scholar 

  53. Sharma A, Singh M (2015) Assessing Alpha activity in Attention and Relaxed State: An EEG Analysis. Paper presented at the 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India,

  54. Shen L, Wang M, Shen R (2009) Affective e-learning: using "emotional" data to improve learning in pervasive learning environment. Educ Technol Soc 12(2):176–186

    Google Scholar 

  55. So WKY, Wong SWH, Mak JN, Chan RHM (2017) An evaluation of mental workload with frontal EEG. PLoS One 12(4):e0174949. https://doi.org/10.1371/journal.pone.0174949

    Article  Google Scholar 

  56. Somers DC, Sheremata SL (2013) Attention maps in the brain. Wiley Interdiscip Rev Cogn Sci 4(4):327–340. https://doi.org/10.1002/wcs.1230

    Article  Google Scholar 

  57. Stern JM, Engel J (2004) Atlas of EEG patterns. Lippincott Williams & Wilkins

  58. Vecchiato G, Maglione AG, Cherubino P, Wasikowska B, Wawrzyniak A, Latuszynska A, Latuszynska M, Nermend K, Graziani I, Leucci MR, Trettel A, Babiloni F (2014) Neurophysiological tools to investigate consumer’s gender differences during the observation of TV commercials. Comput Math Methods Med

  59. Womelsdorf T, Fries P (2007) The role of neuronal synchronization in selective attention. Curr Opin Neurobiol 17(2):154–160. https://doi.org/10.1016/j.conb.2007.02.002

    Article  Google Scholar 

  60. Yan C, Xie H, Liu S, Yin J, Zhang Y, Dai Q (2018) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 19(1):220–229. https://doi.org/10.1109/TITS.2017.2749977

    Article  Google Scholar 

  61. Yaomanee K, Setha P-n, Pasin INA (2012) Brain signal detection methodology for attention training using minimal EEG channels. Paper presented at the IEEE10th International Conference on ICT and Knowledge Engineering,

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Acknowledgements

The authors are grateful for the advices given by Suzanne Hidi, Ulrich Schiefele and Mathew Mitchell at the beginning of experimental phase. The authors would like to thank the assistant researchers during the experiments for their effort in assuring simultaneous EEG recording from all participants. Extended thanks to Universiti Teknologi PETRONAS for the given Graduate Assistantship.

Funding

This research was funded partially by a grant from the Ministry of Higher Education Malaysia for HiCoE for CISIR [0153CA-002].

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Authors and Affiliations

  1. Center for Intelligent Signal and Imaging Research (CISIR), Seri Iskandar, Malaysia

    Areej Babiker, Ibrahima Faye, Wajid Mumtaz & Aamir Saeed Malik

  2. Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Areej Babiker, Wajid Mumtaz & Aamir Saeed Malik

  3. Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Ibrahima Faye

  4. Bio-Science and Engineering Department, College of Systems Engineering and Science, Shibaura Institute of Technology, Saitama, Japan

    Hiroki Sato

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Babiker, A., Faye, I., Mumtaz, W. et al. EEG in classroom: EMD features to detect situational interest of students during learning. Multimed Tools Appl 78, 16261–16281 (2019). https://doi.org/10.1007/s11042-018-7016-z

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