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Neural Indexes of Attention Extracted from EEG Correlate with Elderly Reaction Time in response to an Attentional Task

Authors:

Fatemeh Fahimi,

Cuntai GuanAuthors Info & Claims

ICCSE'18: Proceedings of the 3rd International Conference on Crowd Science and Engineering

Article No.: 33, Pages 1 - 6

https://doi.org/10.1145/3265689.3265722

Published: 28 July 2018 Publication History

Abstract

In the present paper, we analyze electroencephalogram (EEG) signals recorded by a single frontal channel from 105 elderly subjects while they were responding to an attention-demanded task (Stroop color test). The first objective is to discover how post-cue frequency band oscillations of EEG, as neural index of attention, are correlated with elderly response time (RT), as behavioral index of attention. Furthermore, we aim to detect the most informative period of brain activity (EEG) in which the strongest correlations with reaction time exist. Our results show that 1) there is significant negative correlation between alpha gamma ratio (AGR) and response time (p<0.0001), 2) theta beta ratio (TBR) is positively correlated with subjects' response time (p<0.0001) and 3) these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test). Our study provides an insight into the research on analysis and prediction of subject behavior from EEG. Moreover, it has potential to be used in implementation of feasible and efficient single channel EEG-based brain computer interface (BCI) training systems for elderly.

References

[1]

T.-S. Lee, S. J. A. Goh, S. Y. Quek, R. Phillips, C. Guan, Y. B. Cheung, et al., "A Brain-Computer Interface Based Cognitive Training System for Healthy Elderly: A Randomized Control Pilot Study for Usability and Preliminary Efficacy," PLOS ONE, vol. 8, p. e79419, 2013.

[2]

C. G. Lim, T. S. Lee, C. Guan, D. S. S. Fung, Y. Zhao, S. S. W. Teng, et al., "A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder," PLOS ONE, vol. 7, p. e46692, 2012.

[3]

S. Hanslmayr, A. Aslan, T. Staudigl, W. Klimesch, C. S. Herrmann, and K. H. Bauml, "Prestimulus oscillations predict visual perception performance between and within subjects," Neuroimage, vol. 37, pp. 1465--73, Oct 1 2007.

[4]

J. Kamiński, A. Brzezicka, M. Gola, and A. Wróbel, "Beta band oscillations engagement in human alertness process," International Journal of Psychophysiology, vol. 85, pp. 125--128, 2012/07/01/ 2012.

[5]

M. H. MacLean, K. M. Arnell, and K. A. Cote, "Resting EEG in alpha and beta bands predicts individual differences in attentional blink magnitude," Brain and Cognition, vol. 78, pp. 218--229, 2012/04/01/ 2012.

[6]

A. Sharma and M. Singh, "Assessing alpha activity in attention and relaxed state: An EEG analysis," in 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 2015, pp. 508--513.

[7]

W. Klimesch, P. Sauseng, and S. Hanslmayr, "EEG alpha oscillations: the inhibition-timing hypothesis," Brain Res Rev, vol. 53, pp. 63--88, Jan 2007.

[8]

S. Hanslmayr, J. Gross, W. Klimesch, and K. L. Shapiro, "The role of alpha oscillations in temporal attention," Brain Research Reviews, vol. 67, pp. 331--343, 2011/06/24/ 2011.

[9]

W. Klimesch, "alpha-band oscillations, attention, and controlled access to stored information," Trends Cogn Sci, vol. 16, pp. 606--17, Dec 2012.

[10]

Y. I. Jin, J. P. O'Halloran, L. Plon, C. A. Sandman, and S. G. Potkin, "ALPHA EEG PREDICTS VISUAL REACTION TIME," International Journal of Neuroscience, vol. 116, pp. 1035--1044, 2006/01/01 2006.

[11]

C. M. MacLeod, "Half a century of research on the Stroop effect: an integrative review," Psychol Bull, vol. 109, pp. 163--203, Mar 1991.

[12]

C. M. MacLeod and P. A. MacDonald, "Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention," Trends Cogn Sci, vol. 4, pp. 383--391, Oct 1 2000.

[13]

J. R. Stroop, "Studies of interference in serial verbal reactions," Journal of Experimental Psychology, vol. 18, pp. 643--662, 1935.

[14]

F. N. Dyer, "The Stroop phenomenon and its use in the stlldy of perceptual, cognitive, and response processes," Memory & Cognition, vol. 1, pp. 106--120, 1973/06/01 1973.

[15]

T. B. Marie, "Executive Function: The Search for an Integrated Account," Current Directions in Psychological Science, vol. 18, pp. 89--94, 2009/04/01 2009.

[16]

J. Markela-Lerenc, N. Ille, S. Kaiser, P. Fiedler, C. Mundt, and M. Weisbrod, "Prefrontal-cingulate activation during executive control: which comes first?," Cognitive Brain Research, vol. 18, pp. 278--287, 2004/02/01/ 2004.

[17]

M. Liotti, M. G. Woldorff, R. Perez, and H. S. Mayberg, "An ERP study of the temporal course of the Stroop color-word interference effect," Neuropsychologia, vol. 38, pp. 701--11, 2000.

[18]

S. E. Donohue, M. Liotti, R. Perez, and M. G. Woldorff, "Is conflict monitoring supramodal? Spatiotemporal dynamics of cognitive control processes in an auditory Stroop task," Cognitive, affective & behavioral neuroscience, vol. 12, pp. 1--15, 2012.

[19]

F. Fahimi, C. Guan, K. K. Ang, W. B. Goh, and T. S. Lee, "Personalized features for attention detection in children with Attention Deficit Hyperactivity Disorder," in Conf Proc IEEE Eng Med Biol Soc, 2017, pp. 414--417.

Cited By

Loizaga EEyam ABastida LLastra J(2023)A Comprehensive Study of Human Factors, Sensory Principles, and Commercial Solutions for Future Human-Centered Working Operations in Industry 5.0IEEE Access10.1109/ACCESS.2023.328007111(53806-53829)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3280071
Al-Nafjan AAldayel M(2022)Predict Students’ Attention in Online Learning Using EEG DataSustainability10.3390/su1411655314:11(6553)Online publication date: 27-May-2022
https://doi.org/10.3390/su14116553

Index Terms

Neural Indexes of Attention Extracted from EEG Correlate with Elderly Reaction Time in response to an Attentional Task
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Spectral methods
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models

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cover image ACM Other conferences

ICCSE'18: Proceedings of the 3rd International Conference on Crowd Science and Engineering

July 2018

220 pages

ISBN:9781450365871

DOI:10.1145/3265689

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 July 2018

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ICCSE'18

ICCSE'18: The 3rd International Conference on Crowd Science and Engineering

July 28 - 31, 2018

Singapore, Singapore

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ICCSE'18 Paper Acceptance Rate 33 of 89 submissions, 37%;

Overall Acceptance Rate 92 of 247 submissions, 37%

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Cited By

Loizaga EEyam ABastida LLastra J(2023)A Comprehensive Study of Human Factors, Sensory Principles, and Commercial Solutions for Future Human-Centered Working Operations in Industry 5.0IEEE Access10.1109/ACCESS.2023.328007111(53806-53829)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3280071
Al-Nafjan AAldayel M(2022)Predict Students’ Attention in Online Learning Using EEG DataSustainability10.3390/su1411655314:11(6553)Online publication date: 27-May-2022
https://doi.org/10.3390/su14116553

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