Toward Exploiting EEG Input in a Reading Tutor

International Journal of Artificial Intelligence in Education

Volumn 22, Issue 1-2, 2013, Pages 19-38

  Chang, Kai Min ^a   Nelson, Jessica ^a   Pant, Udip ^b   Mostow, Jack ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b BRIGHAM YOUNG UNIVERSITY   (United States)

Author keywords

EEG; frequency band; lexical feature; machine learning; Project LISTEN's Reading Tutor

Indexed keywords

ELECTROENCEPHALOGRAPHY; FREQUENCY BANDS; LEARNING SYSTEMS;

EEG DATUM; INTELLIGENT TUTORS; ISOLATED WORDS; LEXICAL FEATURES; MENTAL STATE; PILOT STUDIES; READING TUTORS; SINGLE-CHANNEL;

TEACHING;

EID: 84882437280     PISSN: 15604292     EISSN: None     Source Type: Journal    
DOI: 10.3233/JAI-130033     Document Type: Conference Paper

References (61)

1. Ackerman, P., Dykman, R., Oglesby, D., & Newton, J. (1994). EEG power spectra of children with dyslexia, slow learners, and normally reading children with ADD during verbal processing. Journal of Learning Disabilities, 27(10), 619-630.
2. Ackerman, P., McPherson, W., Oglesby, D., & Dykman, R. (1998). EEG power spectra of adolescent poor readers. Journal of Learning Disabilities, 31(1), 83-90.
3. Baker, R., D'Mello, S., Rodrigo, M., & Graesser, A. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223-241. doi: 10.1016/j.ijhcs.2009.12.003
4. Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39(3), 445-459.
5. Bartnik, E. A., Blinowska, K. J., & Durka, P. J. (1992). Single evoked potential reconstruction by means of wavelet transform. Biological Cybernetics, 67(2), 175-181.
6. Berka, C., Levendowski, D. J., Lumicao, M. N., Yau, A., Davis, G., Zivkovic, V. T., Vladimir, T., Olmstead, R. E., Tremoulet, P. D., Patrice, D., & Craven, P. L. (2007). EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviation, Space, and Environmental Medicine, 78(Supp 1), B231-B244.
7. Bertrand, O., Bohorquez, J., & Pernier, J. (1994). Time-frequency digital filtering based on an invertible wavelet transform: An application to evoked potentials. IEEE Transactions on Biomedical Engineering, 41(1), 77-88.
8. Blanchard, E., Chalfoun, P., & Frasson, C. (2007). Towards advanced learner modeling: Discussions on quasi real-time adaptation with physiological data. In 7th IEEE International Conference on Advanced Learning Technologies (pp. 809-813). Niigata, Japan.
9. Cavanagh, J. F., Zambrano-Vazquez, L., & Allen, J. J. B. (2012). Theta lingua franca: A common mid-frontal substrate for action monitoring processes. Psychophysiology, 49(2), 220-238.
10. Chalfoun, P. & Frasson, C. (2010). Showing the Positive Influence of Subliminal Cues on Learner's Performance and Intuition: An ERP Study. In V. Aleven, J. Kay & J. Mostow (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems (Vol. 6095, pp. 288-290). Pittsburgh, PA: Springer.
11. Chalfoun, P. & Frasson, C. (2011). Subliminal cues while teaching: HCI technique for enhanced learning. Advances in Human-Computer Interaction - Special issue on subliminal communication in human-computer interaction. doi: 10.1155/2011/968753
12. Chalfoun, P., & Frasson, C. (2012). Cognitive Priming: Assessing the Use of Non-conscious Perception to Enhance Learner's Reasoning Ability. In S. Cerri, W. Clancey, G. Papadourakis & K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems (Vol. 7315, pp. 84-89). Chania, Crete, Greece: Springer Berlin/Heidelberg.
13. Chaouachi, M., Chalfoun, P., Jraidi, I., & Frasson, C. (2010). Affect and mental engagement: towards adaptability for intelligent systems. In Proceedings of the 23rd International FLAIRS Conference. Daytona Beach, FL.
14. Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Editorial: Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter - Special Issue on Learning From Imbalanced Datasets, 6(1), 1-6. doi: 10.1145/1007730.1007733
15. Clarke, A., Barry, R., McCarthy, R., & Selikowitz, M. (2002). EEG Analysis of Children with Attention- Deficit/Hyperactivity Disorder and Comorbid Reading Disabilities. Journal of Learning Disabilities, 35(3), 276-285.
16. CMU. (2008). The CMU Sphinx Group open source speech recognition engines [software at http://cmusphinx.sourceforge.net].
17. Colon, E., Notermans, S., Weerd, J., & Kap, J. (1979). The discriminating role of EEG power spectra in dyslexic children. Journal of Neurology, 221(4), 257-262.
18. Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 33(4), 497-505.
19. Coulson, S., King, J. W., & Kutas, M. (1998). Expect the unexpected: Event-related brain response to morphosyntactic violations. Language and Cognitive Processes, 13(1), 21-58.
20. Crowley, K., Sliney, A., Pitt, I., & Murphy, D. (2010). Evaluating a brain-computer interface to categorise human emotional response, In 10th IEEE International Conference on Advanced Learning Technologies (pp. 276-278). Sousse, Tunisia.
21. D'Mello, S., Craig, S., Fike, K., & Graesser, A. (2009). Responding to learners' cognitive-affective states with supportive and shakeup dialogues. In Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction (pp. 595-604. San Diego, CA.
22. Dalal, S., Baillet, S., Adam, C., Ducorps, A., Schwartz, D., Jerbi, K., Bertrand, O., Garnero, L., Martinerie, J., & Lachaux, J. P. (2009). Simultaneous MEG and intracranial EEG recordings during attentive reading. NeuroImage, 45(4), 1289-1304.
23. Demiralp, T., Ademoglu, A., Schürmann, M., Basar-Eroglu, C., & Basar, E. (1999). Detection of P300 waves in single trials by the wavelet transform (WT). Brain and Language, 66(1), 108-128.
24. Derbali, L., Chalfoun, P., & Frasson, C. (2011). Assessment of learners' attention while overcoming errors and obstacles: An empirical study. In 15th International Conference on Artificial Intelligence in Education (pp. 39-46). Auckland, New Zealand.
25. Derbali, L. & Frasson, C. (2011). Physiological evaluation of attention getting strategies during serious game play. In 15th International Conference on Artificial Intelligence in Education (pp. 447-449). Auckland, New Zealand.
26. Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613- 627.
27. Fein, G., Galin, D., Johnstone, J., Yingling, C., Marcus, M., & Kiersch, M. (1983). EEG power spectra in normal and dyslexic children. I. Reliability during passive conditions. Electroencephalography and Clinical Neurophysiology, 55(4), 399-405. doi: 10.1016/0013-4694(83)90127-X
28. Fell, J., Klaver, P., Lehnertz, K., Grunwald, T., Schaller, C., Elger, C., & Fernández, G. (2001). Human memory formation is accompanied by rhinal-hippocampal coupling and decoupling. Neuroscience, 4(12), 1259- 1264.
29. Fink, A., Grabner, R. H., Neuper, C., & Neubauer, A. C. (2005). EEG alpha band dissociation with increasing task demands. Cognitive Brain Research, 24(2), 252-259.
30. Gluck, K. A., Anderson, J. R., & Douglass, S. (2000). Broader bandwidth in student modeling: What if ITS were "Eye"TS? In 5th International Conference on Intelligent Tutoring Systems (pp. 504-513). Montreal, Canada.
31. Gouvea, A. C., Phillips, C., Kazanina, N., & Poeppel, D. (2010). The linguistic processes underlying the P600. Language and Cognitive Processes, 25(2), 149-188.
32. Graesser, A., McDaniel, B., Chipman, P., Witherspoon, A., D'Mello, S., & Gholson, B. (2006). Detection of emotions during learning with AutoTutor. In 28th Annual Meetings of the Cognitive Science Society (pp. 285-290). Vancouver, Canada.
33. Gruber, T., Müller, M. M., & Keil, K. (2002). Modulation of induced gamma band responses in a perceptual learning task in the human EEG. Cognitive Neuroscience, 14(5), 732-744.
34. 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. International Journal of Psychophysiology, 24(1-2), 161-171.
35. Heraz, A. & Frasson, C. (2007). Predicting the three major dimensions of the learner's emotions from brainwaves. World Academy of Science, Engineering and Technology, 31, 323-329.
36. Heraz, A., Razaki, R., & Frasson, C. (2007). Using machine learning to predict learner emotional state from brain waves. In 7th IEEE International Conference on Advanced Learning Technologies (pp. 853-857.) Niigata, Japan.
37. Herrmann, C. S., Lenz, D., Junge, S., Busch, N. A., & Maess, B. (2004). Memory-matches evoke human gamma responses. BMC Neuroscience, 5(13).
38. Howells, F. M., Stein, D. J., & Russell, V. A. (2010). Perceived mental effort correlates with changes in tonic arousal during attentional tasks. Behavioral and Brain Functions, 6(39).
39. Huang, R. S., Tsai, L. L., & Kuo, C. J. (2001). Selection of valid and reliable EEG features for predicting auditory and visual alertness levels. Proceedings of the National Science Council, Republic of China. Part B, Life science, 25(1), 17-25.
40. Jasper, H. H. (1958). The ten-twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology, 10, 371-375.
41. Jraidi, I., Chalfoun, P., & Frasson, C. (2012). Implicit Strategies for Intelligent Tutoring Systems. In S. Cerri, W. Clancey, G. Papadourakis & K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems (Vol. 7315, pp. 1-10). Chania, Crete, Greece: Springer Berlin/Heidelberg.
42. Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44(4), 978-990.
43. Kutas, M. & Federmeier, K.D. (2000). Electrophysiology reveals semantic memory use in language comprehension. Trends in Cognitive Sciences, 4(12), 463-470.
44. Kutas, M. & Hillyard, S. A. (1980). Event-related brain potentials to semantically inappropriate and surprisingly large words. Biological Psychology, 11(2), 99-116.
45. Lachaux, J. P., Jung, J., Mainy, N., Dreher, J. C., Bertrand, O., Baciu, M., Minotti, L., Hoffmann, D., & Kahane, P. (2008). Silence is golden: Transient neural deactivation in the prefrontal cortex during attentive reading. Cerebral Cortex, 18(2), 443-450.
46. Laszlo, S. & Federmeier, K. D. (2011). The N400 as a snapshot of interactive processing: Evidence from regression analyses of orthographic neighbor and lexical associate effects. Psychophysiology, 48, 176-186.
47. Luo, A. & Sullivan, T. J. (2010). A user-friendly SSVEP-based brain-computer interface using a time-domain classifier. Neural Engineering, 7(2).
48. Lutsyuk, N. V., Eismont, E. V., & Pavlenko, V. B. (2006). Correlation of the characteristics of EEG potentials with the indices of attention in 12- to 13-year-old children. Neurophysiology, 38(3), 209-216.
49. Mainy, N., Kahane, P., Minotti, L., Hoffmann, D., Bertrand, O., & Lachaux, J. P. (2007). Neural correlates of consolidation in working memory. Human Brain Mapping, 28(3), 183-193.
50. Marosi, E., Bazán, O., Yañez, G., Bernal, J., Fernández, T., Rodríguez, M., Silva, J., & Reyes, A. (2002). Narrowband spectral measurements of EEG during emotional tasks. International Journal of Neuroscience, 112(7), 871-891.
51. Mitchell, T., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M. A., & Newman, S. D. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57(1-2), 145-175.
52. Mostow, J. & Beck, J. E. (2007). When the rubber meets the road: Lessons from the in-school adventures of an automated Reading Tutor that listens. In B. Schneider & S.K. McDonald (Eds.), Scale-Up in Education (Vol. 2, pp. 183-200). Lanham, MD: Rowman & Littlefield Publishers.
53. Mostow, J. & Beck, J. E. (2009).Why, what, and how to log? Lessons from LISTEN. In 2nd International Conference on Educational Data Mining (pp. 269-278). Córdoba, Spain.
54. Mostow, J., Chang, K. M., & Nelson, J. (2011).Toward exploiting EE Ginput in a Reading Tutor. In 15th International Conference on Artificial Intelligence in Education (pp. 230-237). Auckland, NZ.
55. Mostow, J. & Jang, H. (2012). Generating diagnostic multiple choice comprehension cloze questions. In 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 7thWorkshop on Innovative Use of NLP for Building Educational Applications, Montréal, Canada.
56. Mota, S. & Picard, R.W. (2003). Automated Posture Analysis for Detecting Learner's Interest Level. In Computer Vision and Pattern Recognition Workshop (p. 49).
57. NeuroSky. (2009). NeuroSky's eSense™ meters and dtection of mntal sate: Neurosky, Inc.
58. Ng, A. Y. & Jordan, M. I. (2002). On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems.
59. Picton, T.W. (1992). The P300 wave of the human event-related potential. Clinical Neurophysiology, 9(4), 456-479.
60. Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: Recognising and responding to student affect. from Inderscience Enterprises Ltd.
61. Yasui, Y. (2009). A brainwave signal measurement and data processing technique for daily life applications. Physiological Anthropology, 28(3), 145-150.