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Volume 11, Issue 1 (Winter 2022) |
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Estimation of Attention Indices in IVA Tests Using Optical Flow in ERP Brain Maps
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Ali Esmaili Jami   , Mohammad Ali Khalilzadeh * , Majid Ghoshuni , Mohammad Mahdi Khalilzadeh |
| Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran , makhlilzadeh@mshdiau.ac.ir |
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Abstract: (707 Views) |
Introduction: The evaluation of attention as one of the human cognitive abilities is of great importance. Although methods for assessing attention ability have been developed and used, the presence of interfering factors has reduced their validity and reliability. Therefore, using the direct outputs of the brain system and analyzing its function in cognitive activities has become very important. This research tries to identify a relationship between event-related potential (ERP) and integrated visual and auditory (IVA) test indices. Materials and Methods: EEG signals (19 channels) and IVA tests of 28 healthy volunteers (22 men and 6 women with an age range of 22 to 32 years) were recorded simultaneously. ERPs to auditory and visual stimuli were obtained by the simultaneous averaging method of extraction and brain topography for each stimulus. Using the Lucas-Kanade method, the optical flow was obtained on brain maps and movement vectors were identified and drawn in consecutive maps. The motion vectors show the location and the number of changes in the activity of each map compared to the other samples. Based on the local connectivity criteria, features were extracted from the brain graphs. The indicators of attention and response control, including vigilance, concentration, speed, caution, stability, endurance, and understanding, were obtained based on the IVA test and were estimated by the support vector-regression machine. Results: In order to evaluate the regression, the correlation index was calculated, which are vigilance (0/80), Focus (0/81), Speed (0/85), Prudence (0/88), consistency (0/90), Stamina (0/85), and comprehension (0/80). Conclusion: According to the high correlation coefficients obtained between the local characteristics of optical flow extracted from the brain graph of the ERP signals and the attention indicators in the IVA test, it can be suggested that there is a significant relationship between the electrical activity of the brain and the ability to pay attention.
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| Keywords: Attention, Evoked Potentials, Optic Flow, Neuropsychological Tests |
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Full-Text [PDF 1725 kb]
(947 Downloads)
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Type of Study: Research --- Open Access, CC-BY-NC |
Subject:
Cognitive Neuroscience
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1. Nani A, Manuello J, Mancuso L, Liloia D, Costa T, Cauda F. The neural correlates of consciousness and attention: two sister processes of the brain. Frontiers in Neuroscience. 2019:1169. [ DOI:10.3389/fnins.2019.01169] 2. Moslemi B, Azmodeh M, Tabatabaei M, Alivandi Vafa M. The Effect of Transcranial Direct Current Stimulation on Dorsolateral Prefrontal Cortex: a Review of its Role on Cognitive Functions. The Neuroscience Journal of Shefaye Khatam. 2019;8(1):129-44. [ DOI:10.29252/shefa.8.1.129] 3. Ptak R, Schnider A. The attention network of the human brain: relating structural damage associated with spatial neglect to functional imaging correlates of spatial attention. Neuropsychologia. 2011;49(11):3063-70. [ DOI:10.1016/j.neuropsychologia.2011.07.008] 4. Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nature neuroscience. 2016;19(1):165-71. [ DOI:10.1038/nn.4179] 5. Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clinical neurophysiology. 2003;114(2):171-83. [ DOI:10.1016/S1388-2457(02)00362-0] 6. Moezzi S, Ghoshuni M, Amiri M. Assessment of the Effect of Transcranial Direct Current Stimulations (tDCS) in Focused Attention Enhancement Using Event-Related Potentials. Shefaye Khatam. 2020; 9 (1) :25-35. [ DOI:10.52547/shefa.9.1.25] 7. Bashiri A, Shahmoradi L, Beigy H, Savareh BA, Nosratabadi M, N Kalhori SR, et al. Quantitative EEG features selection in the classification of attention and response control in the children and adolescents with attention deficit hyperactivity disorder. Future science OA. 2018;4(5):FSO292. [ DOI:10.4155/fsoa-2017-0138] 8. Rabin LA, Paolillo E, Barr WB. Stability in test-usage practices of clinical neuropsychologists in the United States and Canada over a 10-year period: A follow-up survey of INS and NAN members. Archives of Clinical Neuropsychology. 2016 May 1;31(3):206-30. [ DOI:10.1093/arclin/acw007] 9. Kratz O, Studer P, Malcherek S, Erbe K, Moll GH, Heinrich H. Attentional processes in children with ADHD: an event-related potential study using the attention network test. International journal of Psychophysiology. 2011;81(2):82-90. [ DOI:10.1016/j.ijpsycho.2011.05.008] 10. Mueller A, Candrian G, Grane VA, Kropotov JD, Ponomarev VA, Baschera G-M. Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study. Nonlinear biomedical physics. 2011;5(1):1-18. [ DOI:10.1186/1753-4631-5-5] 11. Rahimi M, Moradi MH, Ghassemi F. Comparison of brain effective connectivity in different states of attention and consciousness based on EEG signals. Biomedical Signal Processing and Control. 2019;51:393-400. [ DOI:10.1016/j.bspc.2019.02.002] 12. Sanchez-Lopez J, Silva-Pereyra J, Fernandez T. Sustained attention in skilled and novice martial arts athletes: a study of event-related potentials and current sources. PeerJ. 2016;4:e1614. [ DOI:10.7717/peerj.1614] 13. Ghafourian P, Ghoshuni M, Vosoogh I. Evaluation of Exam Anxiety in Healthy Subjects using Brain Signals Analysis. The Neuroscience Journal of Shefaye Khatam. 2020;8(3):61-9. [ DOI:10.29252/shefa.8.3.61] 14. sadeghiyan f, Hasani H, Jafari M. Dimension Reduction in fMRI Images based on Metaheuristic Algorithm to Diagnose Autism. The Neuroscience Journal of Shefaye Khatam. 2021;9(3):1-11. [ DOI:10.52547/shefa.9.3.1] 15. Lim SH, Nisar H, Thee KW, Yap VV. A novel method for tracking and analysis of EEG activation across brain lobes. Biomedical Signal Processing and Control. 2018;40:488-504. [ DOI:10.1016/j.bspc.2017.06.017] 16. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059-69. [ DOI:10.1016/j.neuroimage.2009.10.003] 17. Mohammadpoor M, Alizadeh A. Using Support Vector Machines as an Intelligent Algorithm for Detecting Seizures from EEG Signals. The Neuroscience Journal of Shefaye Khatam. 2021;9(2):1-9. [ DOI:10.52547/shefa.9.2.1] 18. Ghassemi F, Moradi MH, TEHRANI DM, Abootalebi V, KHORRAMI BA, Mohammadian A. Relations between Levels of Sustained Attention and Event-Related-Potentials. 19. Kim J, Lee Y, Han D, Min K, Kim D, Lee C. The utility of quantitative electroencephalography and integrated visual and auditory continuous performance test as auxiliary tools for the attention deficit hyperactivity disorder diagnosis. Clinical Neurophysiology. 2015;126(3):532-40. [ DOI:10.1016/j.clinph.2014.06.034] 20. Ghoshuni M, Gharibi H, Vosough I. Predicting attention deficit and impulsivity in children with ADHD using brain signal analysis and integrated visual and auditory (IVA) test. Neuropsychology. 2021 Oct 23;7(3).
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Esmaili Jami A, Khalilzadeh M A, Ghoshuni M, Khalilzadeh M M. Estimation of Attention Indices in IVA Tests Using Optical Flow in ERP Brain Maps. Shefaye Khatam 2022; 11 (1) :46-56 URL: http://shefayekhatam.ir/article-1-2330-en.html
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