Abstract
Eye movement patterns are found to reveal human cognitive and mental states that can not be easily measured by other biological signals. With the rapid development of eye tracking technologies, there are growing interests in analysing gaze data to infer information about people’ cognitive states, tasks and activities performed in naturalistic environments. In this paper, we investigate the link between eye movements and cognitive function. We conducted experiments to record subject’s eye movements during video watching. By using computational methods, we identified eye movement features that are correlated to people’s cognitive health measures obtained through the standard cognitive tests. Our results show that it is possible to infer people’s cognitive function by analysing natural gaze behaviour. This work contributes an initial understanding of monitoring cognitive deterioration and dementia with automatic eye movement analysis.
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References
Anderson, T.J., MacAskill, M.R.: Eye movements in patients with neurodegenerative disorders. Nat. Rev. Neurol. 9(2), 74–85 (2013)
Bednarik, R., Vrzakova, H., Hradis, M.: What do you want to do next: a novel approach for intent prediction in gaze-based interaction. In: Procedings of ETRA 2012, ETRA ’12, pp. 83–90. ACM, New York, NY, USA (2012)
Benson, P.J., Beedie, S.A., Shephard, E., Giegling, I., Rujescu, D., Clair, D.S.: Simple viewing tests can detect eye movement abnormalities that distinguish schizophrenia cases from controls with exceptional accuracy. Biol. Psychiatry 72(9), 716–724 (2012). Cortical Inhibition Deficits in Schizophrenia
Borji, A., Itti, L.: Defending yarbus: eye movements reveal observers’ task. J. Vision 14(3(29)), 1–22 (2014)
Bulling, A., Roggen, D., Trster, G.: What’s in the eyes for context-awareness? IEEE Pervasive Comput. 10(2), 48–57 (2011)
Crabb, D.P., Smith, N.D., Zhu, H.: What’s on tv? detecting age-related neurodegenerative eye disease using eye movement scanpaths. Frontiers Aging Neurosci. 6(312) (2014)
Crawford, T.J., Higham, S., Mayes, J., Dale, M., Shaunak, S., Lekwuwa, G.: The role of working memory and attentional disengagement on inhibitory control: effects of aging and alzheimer’s disease. Age 35(5), 1637–1650 (2013)
Crawford, T.J., Higham, S., Renvoize, T., Patel, J., Dale, M., Suriya, A., Tetley, S.: Inhibitory control of saccadic eye movements and cognitive impairment in alzheimers disease. Biol. Psychiatry 57(9), 1052–1060 (2005)
Di Stasi, L.L., Renner, R., Staehr, P., Helmert, J.R., Velichkovsky, B.M., Cañas, J.J., Catena, A., Pannasch, S.: Saccadic peak velocity sensitivity to variations in mental workload. Aviat. Space Environ. Med. 81(4), 413–417 (2010)
Grober, E., Buschke, H.: Genuine memory deficits in dementia. Dev. neuropsychol. 3(1), 13–36 (1987)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer New York Inc., New York, NY, USA, Springer Series in Statistics (2001)
Henderson, J.M., Shinkareva, S.V., Wang, J., Luke, S.G., Olejarczyk, J.: Predicting cognitive state from eye movements. PLoS ONE 8(5), e64937 (2013)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)
Jang, Y.M., Lee, S., Mallipeddi, R., Kwak, H.W., Lee, M.: Recognition of human’s implicit intention based on an eyeball movement pattern analysis. In: Lu, B.L., Zhang, L., Kwok, J. (eds.) Neural Information Processing. Lecture Notes in Computer Science, vol. 7062, pp. 138–145. Springer, Heidelberg (2011)
Jimison, H., Jessey, N., McKanna, J., Zitzelberger, T., Kaye, J.: Monitoring computer interactions to detect early cognitive impairment in elders. In: 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2, pp. 75–78. IEEE (2006)
Kardan, O., Berman, M.G., Yourganov, G., Schmidt, J., Henderson, J.M.: Classifying mental states from eye movements during scene viewing (2015)
Knapp, M., Prince, M., Albanese, E., Banerjee, S., Dhanasiri, S., Fernandez, J., Ferri, C., Snell, T., Stewart, R.: Dementia uk: report to the alzheimer’s society. Kings College London and London School of Economics and Political Science (2007)
Nasreddine, Z.S., Phillips, N.A., Bdirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J.L., Chertkow, H.: The montreal cognitive assessment, moca: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53(4), 695–699 (2005)
Schleicher, R., Galley, N., Briest, S., Galley, L.: Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? Ergonomics 51(7), 982–1010 (2008)
Steichen, B., Conati, C., Carenini, G.: Inferring visualization task properties, user performance, and user cognitive abilities from eye gaze data. ACM Trans. Interact. Intell. Syst. 4(2), 11:1–11:29 (2014)
Tseng, P.H., Cameron, I., Pari, G., Reynolds, J., Munoz, D., Itti, L.: High-throughput classification of clinical populations from natural viewing eye movements. J. Neurol. 260(1), 275–284 (2013)
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The work described in this paper is funded by EPSRC project EP/M006255/1 Monitoring Of Dementia using Eye Movements (MODEM).
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Zhang, Y., Wilcockson, T., Kim, K.I., Crawford, T., Gellersen, H., Sawyer, P. (2016). Monitoring Dementia with Automatic Eye Movements Analysis. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_26
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DOI: https://doi.org/10.1007/978-3-319-39627-9_26
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