Exploring Brain Activity in Different Mental Cognitive Workloads
-
Sahar Oftadeh Balani
,
Ali Fawzi Al-Hussainy
,
Alhan Abd Al-Hassan Shalal
,
Mohammed Ubaid
,
Zinab Aluquaily
,
Jaafar Alamoori
,
Saeid Motevalli
Abstract
Objective: Understanding neural mechanisms underlying cognitive workload is crucial for advancing our knowledge of human cognition and mental processes. In this study, we utilized electroencephalography (EEG) analysis to investigate brain activity associated with varying mental cognitive workloads from a psychological perspective.
Method: We employed a publicly accessible EEG dataset consisting of a cohort of 36 healthy volunteers (75% female), aged 18 to 26 years, while the participants were at rest or engaged in an arithmetic task to explore mental cognitive workload. After preprocessing to reduce noise and various artifacts and to obtain a clean signal for every subject, functional connectivity and complexity features were calculated from EEGs through the coherence and permutation entropy algorithms, respectively. Then, repeated measures analysis of variance (ANOVA) was conducted to assess the differences in complexity and connectivity measures across various brain regions between the rest and task states.
Results: Brain sites showed significant within-subject effects, and the interaction between states and channels was significant for connectivity values (F = 3.68, P = 0.034). Post hoc comparisons indicated that FP1-F7, FP1-F8 and FP1-Fz connectivity were significantly lower during the task state compared to the rest state (P < 0.05). Moreover, F4-P3, F4-P4, FP1-O1, FP2-O2, F3-O1, F4-O1, F8-O1, C4-O1, F3-O2, F4-O2, F7-O2, F8-O2, Fz-O1, Fz-O2, Cz-O1 and Fz-P4 connectivity were significantly higher during the arithmetic task state (P < 0.05). Furthermore, brain sites showed significant within-subject effects and the interaction between states and channels was significant for entropy values (F = 3.50, P = 0.041). Post hoc comparisons indicated that the permutation entropy was significantly higher in the FP1, T3, T4, P4 and Pz channels during the arithmetic task compared to the rest state (P < 0.05).
Conclusion: During arithmetic tasks, the increased connectivity in the frontoparietal and frontooccipital networks and heightened complexity in the prefrontal, temporal and parietal lobes reflect the collaborative engagement of brain areas specialized in numerical processing, attention, working memory, cognitive control, and visual-spatial cognition. These changes in connectivity and complexity facilitate the integration of multiple cognitive processes essential for effective arithmetic problem-solving.
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8. Ekhlasi A, Motie Nasrabadi A, Mohammadi MR. Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy. Iran J Psychiatry. 2021;16(4):374-82.
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12. Ranganath C, Ritchey M. Two cortical systems for memory-guided behaviour. Nat Rev Neurosci. 2012;13(10):713-26.
13. Soltani A, Koechlin E. Computational models of adaptive behavior and prefrontal cortex. Neuropsychopharmacology. 2022;47(1):58-71.
14. Ji Z, Tang J, Wang Q, Xie X, Liu J, Yin Z. Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals. Comput Methods Programs Biomed. 2023;230:107352.
15. Afzali A, Khaleghi A, Hatef B, Akbari Movahed R, Pirzad Jahromi G. Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals. Waves in Random and Complex Media. 2023:1-16.
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19. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
20. Zhang G, Cui Y, Zhang Y, Cao H, Zhou G, Shu H, et al. Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level. Neuroimage. 2021;237:118166.
21. Nee DE. Integrative frontal-parietal dynamics supporting cognitive control. Elife. 2021;10: e57244.
22. Saint Amour di Chanaz L, Pérez-Bellido A, Wu X, Lozano-Soldevilla D, Pacheco-Estefan D, Lehongre K, et al. Gamma amplitude is coupled to opposed hippocampal theta-phase states during the encoding and retrieval of episodic memories in humans. Curr Biol. 2023;33(9):1836-43.e6.
23. Köster M. The theta-gamma code in predictive processing and mnemonic updating. Neurosci Biobehav Rev. 2024;158:105529.
24. Herweg NA, Solomon EA, Kahana MJ. Theta Oscillations in Human Memory. Trends Cogn Sci. 2020;24(3):208-27.
25. Khaleghi A, Sheikhani A, Mohammadi MR, Moti Nasrabadi A. Evaluation of Cerebral Cortex Function in Clients with Bipolar Mood Disorder I (BMD I) Compared With BMD II Using QEEG Analysis. Iran J Psychiatry. 2015;10(2):93-9.
26. Trajkovic J, Di Gregorio F, Avenanti A, Thut G, Romei V. Two Oscillatory Correlates of Attention Control in the Alpha-Band with Distinct Consequences on Perceptual Gain and Metacognition. J Neurosci. 2023;43(19):3548-56.
27. Xiao W, Manyi G, Khaleghi A. Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder. J Psychiatr Res. 2022;151:368-76.
28. Raud L, Huster RJ. The Temporal Dynamics of Response Inhibition and their Modulation by Cognitive Control. Brain Topogr. 2017;30(4):486-501.
29. Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, et al. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage. 2022;257:119056.
30. Sorinas J, Fernandez-Troyano JC, Ferrandez JM, Fernandez E. Cortical Asymmetries and Connectivity Patterns in the Valence Dimension of the Emotional Brain. Int J Neural Syst. 2020;30(5):2050021.
31. Wirsich J, Amico E, Giraud AL, Goñi J, Sadaghiani S. Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition. Netw Neurosci. 2020;4(3):658-77.
32. Bassett DS, Sporns O. Network neuroscience. Nat Neurosci. 2017;20(3):353-64.
33. Hearne LJ, Cocchi L, Zalesky A, Mattingley JB. Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning. J Neurosci. 2017;37(35):8399-411.
34. Figueira JSB, David IPA, Lobo I, Pacheco LB, Pereira MG, de Oliveira L, et al. Effects of load and emotional state on EEG alpha-band power and inter-site synchrony during a visual working memory task. Cogn Affect Behav Neurosci. 2020;20(5):1122-32.
35. Yin S, Li Y, Chen A. Functional coupling between frontoparietal control subnetworks bridges the default and dorsal attention networks. Brain Struct Funct. 2022;227(7):2243-60.
36. Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, et al. Electroencephalograms during mental arithmetic task performance. Data. 2019;4(1):14.
37. Khaleghi N, Hashemi S, Peivandi M, Ardabili SZ, Behjati M, Sheykhivand S, et al. EEG-based functional connectivity analysis of brain abnormalities: A review study. Inform Med Unlocked. 2024:101476.
38. Wu L. Classification of Coherence Indices Extracted from EEG Signals of Mild and Severe Autism. International Journal of Advanced Computer Science and Applications. 2023;14(9).
39. Fischer MHF, Zibrandtsen IC, Høgh P, Musaeus CS. Systematic Review of EEG Coherence in Alzheimer's Disease. J Alzheimers Dis. 2023;91(4):1261-72.
40. Hernández RM, Ponce-Meza JC, Saavedra-López M, Campos Ugaz WA, Chanduvi RM, Monteza WC. Brain Complexity and Psychiatric Disorders. Iran J Psychiatry. 2023;18(4):493-502.
41. Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A. Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study. Iran J Psychiatry. 2023;18(2):127-33.
42. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6:66-73.
43. Bratu IF, Makhalova J, Garnier E, Villalon SM, Jegou A, Bonini F, et al. Permutation entropy-derived parameters to estimate the epileptogenic zone network. Epilepsia. 2024;65(2):389-401.
44. Fide E, Polat H, Yener G, Özerdem MS. Effects of Pharmacological Treatments in Alzheimer's Disease: Permutation Entropy-Based EEG Complexity Study. Brain Topogr. 2023;36(1):106-18.
45. Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. Comput Methods Programs Biomed. 2021;206:106116.
46. Zhao H, Li X, Karolis V, Feng Y, Niu H, Butterworth B. Arithmetic learning modifies the functional connectivity of the fronto-parietal network. Cortex. 2019;111:51-62.
47. Istomina A, Arsalidou M. Add, subtract and multiply: Meta-analyses of brain correlates of arithmetic operations in children and adults. Dev Cogn Neurosci. 2024;69:101419.
48. Friedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47(1):72-89.
49. Vandecruys F, Vandermosten M, De Smedt B. The inferior fronto-occipital fasciculus correlates with early precursors of mathematics and reading before the start of formal schooling. Cortex. 2024;174:149-63.
50. Moore D, Jung M, Hillman CH, Kang M, Loprinzi PD. Interrelationships between exercise, functional connectivity, and cognition among healthy adults: A systematic review. Psychophysiology. 2022;59(6):e14014.
51. Moeller K, Willmes K, Klein E. A review on functional and structural brain connectivity in numerical cognition. Front Hum Neurosci. 2015;9:227.
52. Dattola S, Bonanno L, Ielo A, Quercia A, Quartarone A, La Foresta F. Brain Active Areas Associated with a Mental Arithmetic Task: An eLORETA Study. Bioengineering (Basel). 2023;10(12):1388.
53. Xu S, Li Y, Liu J. The Neural Correlates of Computational Thinking: Collaboration of Distinct Cognitive Components Revealed by fMRI. Cereb Cortex. 2021;31(12):5579-97.
54. Cole MW, Laurent P, Stocco A. Rapid instructed task learning: a new window into the human brain's unique capacity for flexible cognitive control. Cogn Affect Behav Neurosci. 2013;13(1):1-22.
55. Kutter EF, Boström J, Elger CE, Nieder A, Mormann F. Neuronal codes for arithmetic rule processing in the human brain. Curr Biol. 2022;32(6):1275-84.e4.
56. Cui J, Li L, Li M, Siegler R, Zhou X. Middle temporal cortex is involved in processing fractions. Neurosci Lett. 2020;725:134901.
57. Braunsdorf M, Blazquez Freches G, Roumazeilles L, Eichert N, Schurz M, Uithol S, et al. Does the temporal cortex make us human? A review of structural and functional diversity of the primate temporal lobe. Neurosci Biobehav Rev. 2021;131:400-10.
58. Castaldi E, Vignaud A, Eger E. Mapping subcomponents of numerical cognition in relation to functional and anatomical landmarks of human parietal cortex. Neuroimage. 2020;221:117210.
2. Huang J, Pugh ZH, Kim S, Nam CS. Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling. Comput Human Behav. 2024;152:108043.
3. Carissoli C, Negri L, Bassi M, Storm FA, Delle Fave A. Mental workload and human-robot interaction in collaborative tasks: a scoping review. Int J Hum Comput. 2023:1-20.
4. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26-36.
5. Jin L, Qu H, Pang L, Zhang Z, Lyu Z. Identifying stable EEG patterns over time for mental workload recognition using transfer DS-CNN framework. Biomedical Signal Processing and Control. 2024;89:105662.
6. Khaleghi A, Shahi K, Saidi M, Babaee N, Kaveh R, Mohammadian A. Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition. Cogn Neurodyn. 2024:1-12.
7. Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol. 2022;128(1):197-217.
8. Ekhlasi A, Motie Nasrabadi A, Mohammadi MR. Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy. Iran J Psychiatry. 2021;16(4):374-82.
9. Moeini M, Khaleghi A, Amiri N, Niknam Z. Quantitative electroencephalogram (QEEG) Spectrum Analysis of Patients with Schizoaffective Disorder Compared to Normal Subjects. Iran J Psychiatry. 2014;9(4):216-21.
10. Campos-Ugaz WA, Palacios Garay JP, Rivera-Lozada O, Alarcón Diaz MA, Fuster-Guillén D, Tejada Arana AA. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges. Iran J Psychiatry. 2023;18(2):237-47.
11. Xue A, Kong R, Yang Q, Eldaief MC, Angeli PA, DiNicola LM, et al. The detailed organization of the human cerebellum estimated by intrinsic functional connectivity within the individual. J Neurophysiol. 2021;125(2):358-84.
12. Ranganath C, Ritchey M. Two cortical systems for memory-guided behaviour. Nat Rev Neurosci. 2012;13(10):713-26.
13. Soltani A, Koechlin E. Computational models of adaptive behavior and prefrontal cortex. Neuropsychopharmacology. 2022;47(1):58-71.
14. Ji Z, Tang J, Wang Q, Xie X, Liu J, Yin Z. Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals. Comput Methods Programs Biomed. 2023;230:107352.
15. Afzali A, Khaleghi A, Hatef B, Akbari Movahed R, Pirzad Jahromi G. Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals. Waves in Random and Complex Media. 2023:1-16.
16. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
17. Syaifulloh MK, Hanafi M, Ardyanto TD, Wiyono N, Budianto P, Jumiatmoko J. Electroencephalography (EEG) Frontal Alpha Asymmetry Index as an Indicator of Children's Emotions in the Three Quran Learning Methods: Visual, Auditory, and Memory. Iran J Psychiatry. 2023;18(1):93-6.
18. Pallathadka H, Gardanova ZR, Al-Tameemi AR, Al-Dhalimy AMB, Kadhum EH, Redhee AH. Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study. Iran J Psychiatry. 2024;19(3):327-36.
19. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
20. Zhang G, Cui Y, Zhang Y, Cao H, Zhou G, Shu H, et al. Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level. Neuroimage. 2021;237:118166.
21. Nee DE. Integrative frontal-parietal dynamics supporting cognitive control. Elife. 2021;10: e57244.
22. Saint Amour di Chanaz L, Pérez-Bellido A, Wu X, Lozano-Soldevilla D, Pacheco-Estefan D, Lehongre K, et al. Gamma amplitude is coupled to opposed hippocampal theta-phase states during the encoding and retrieval of episodic memories in humans. Curr Biol. 2023;33(9):1836-43.e6.
23. Köster M. The theta-gamma code in predictive processing and mnemonic updating. Neurosci Biobehav Rev. 2024;158:105529.
24. Herweg NA, Solomon EA, Kahana MJ. Theta Oscillations in Human Memory. Trends Cogn Sci. 2020;24(3):208-27.
25. Khaleghi A, Sheikhani A, Mohammadi MR, Moti Nasrabadi A. Evaluation of Cerebral Cortex Function in Clients with Bipolar Mood Disorder I (BMD I) Compared With BMD II Using QEEG Analysis. Iran J Psychiatry. 2015;10(2):93-9.
26. Trajkovic J, Di Gregorio F, Avenanti A, Thut G, Romei V. Two Oscillatory Correlates of Attention Control in the Alpha-Band with Distinct Consequences on Perceptual Gain and Metacognition. J Neurosci. 2023;43(19):3548-56.
27. Xiao W, Manyi G, Khaleghi A. Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder. J Psychiatr Res. 2022;151:368-76.
28. Raud L, Huster RJ. The Temporal Dynamics of Response Inhibition and their Modulation by Cognitive Control. Brain Topogr. 2017;30(4):486-501.
29. Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, et al. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage. 2022;257:119056.
30. Sorinas J, Fernandez-Troyano JC, Ferrandez JM, Fernandez E. Cortical Asymmetries and Connectivity Patterns in the Valence Dimension of the Emotional Brain. Int J Neural Syst. 2020;30(5):2050021.
31. Wirsich J, Amico E, Giraud AL, Goñi J, Sadaghiani S. Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition. Netw Neurosci. 2020;4(3):658-77.
32. Bassett DS, Sporns O. Network neuroscience. Nat Neurosci. 2017;20(3):353-64.
33. Hearne LJ, Cocchi L, Zalesky A, Mattingley JB. Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning. J Neurosci. 2017;37(35):8399-411.
34. Figueira JSB, David IPA, Lobo I, Pacheco LB, Pereira MG, de Oliveira L, et al. Effects of load and emotional state on EEG alpha-band power and inter-site synchrony during a visual working memory task. Cogn Affect Behav Neurosci. 2020;20(5):1122-32.
35. Yin S, Li Y, Chen A. Functional coupling between frontoparietal control subnetworks bridges the default and dorsal attention networks. Brain Struct Funct. 2022;227(7):2243-60.
36. Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, et al. Electroencephalograms during mental arithmetic task performance. Data. 2019;4(1):14.
37. Khaleghi N, Hashemi S, Peivandi M, Ardabili SZ, Behjati M, Sheykhivand S, et al. EEG-based functional connectivity analysis of brain abnormalities: A review study. Inform Med Unlocked. 2024:101476.
38. Wu L. Classification of Coherence Indices Extracted from EEG Signals of Mild and Severe Autism. International Journal of Advanced Computer Science and Applications. 2023;14(9).
39. Fischer MHF, Zibrandtsen IC, Høgh P, Musaeus CS. Systematic Review of EEG Coherence in Alzheimer's Disease. J Alzheimers Dis. 2023;91(4):1261-72.
40. Hernández RM, Ponce-Meza JC, Saavedra-López M, Campos Ugaz WA, Chanduvi RM, Monteza WC. Brain Complexity and Psychiatric Disorders. Iran J Psychiatry. 2023;18(4):493-502.
41. Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A. Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study. Iran J Psychiatry. 2023;18(2):127-33.
42. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6:66-73.
43. Bratu IF, Makhalova J, Garnier E, Villalon SM, Jegou A, Bonini F, et al. Permutation entropy-derived parameters to estimate the epileptogenic zone network. Epilepsia. 2024;65(2):389-401.
44. Fide E, Polat H, Yener G, Özerdem MS. Effects of Pharmacological Treatments in Alzheimer's Disease: Permutation Entropy-Based EEG Complexity Study. Brain Topogr. 2023;36(1):106-18.
45. Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. Comput Methods Programs Biomed. 2021;206:106116.
46. Zhao H, Li X, Karolis V, Feng Y, Niu H, Butterworth B. Arithmetic learning modifies the functional connectivity of the fronto-parietal network. Cortex. 2019;111:51-62.
47. Istomina A, Arsalidou M. Add, subtract and multiply: Meta-analyses of brain correlates of arithmetic operations in children and adults. Dev Cogn Neurosci. 2024;69:101419.
48. Friedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47(1):72-89.
49. Vandecruys F, Vandermosten M, De Smedt B. The inferior fronto-occipital fasciculus correlates with early precursors of mathematics and reading before the start of formal schooling. Cortex. 2024;174:149-63.
50. Moore D, Jung M, Hillman CH, Kang M, Loprinzi PD. Interrelationships between exercise, functional connectivity, and cognition among healthy adults: A systematic review. Psychophysiology. 2022;59(6):e14014.
51. Moeller K, Willmes K, Klein E. A review on functional and structural brain connectivity in numerical cognition. Front Hum Neurosci. 2015;9:227.
52. Dattola S, Bonanno L, Ielo A, Quercia A, Quartarone A, La Foresta F. Brain Active Areas Associated with a Mental Arithmetic Task: An eLORETA Study. Bioengineering (Basel). 2023;10(12):1388.
53. Xu S, Li Y, Liu J. The Neural Correlates of Computational Thinking: Collaboration of Distinct Cognitive Components Revealed by fMRI. Cereb Cortex. 2021;31(12):5579-97.
54. Cole MW, Laurent P, Stocco A. Rapid instructed task learning: a new window into the human brain's unique capacity for flexible cognitive control. Cogn Affect Behav Neurosci. 2013;13(1):1-22.
55. Kutter EF, Boström J, Elger CE, Nieder A, Mormann F. Neuronal codes for arithmetic rule processing in the human brain. Curr Biol. 2022;32(6):1275-84.e4.
56. Cui J, Li L, Li M, Siegler R, Zhou X. Middle temporal cortex is involved in processing fractions. Neurosci Lett. 2020;725:134901.
57. Braunsdorf M, Blazquez Freches G, Roumazeilles L, Eichert N, Schurz M, Uithol S, et al. Does the temporal cortex make us human? A review of structural and functional diversity of the primate temporal lobe. Neurosci Biobehav Rev. 2021;131:400-10.
58. Castaldi E, Vignaud A, Eger E. Mapping subcomponents of numerical cognition in relation to functional and anatomical landmarks of human parietal cortex. Neuroimage. 2020;221:117210.
| Files | ||
| Issue | Vol 19 No 4 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijps.v19i4.16549 | |
| Keywords | ||
| Brain Complexity Analysis Cognition Electroencephalography | ||
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How to Cite
1.
Oftadeh Balani S, Al-Hussainy A, Shalal A, Ubaid M, Aluquaily Z, Alamoori J, Motevalli S. Exploring Brain Activity in Different Mental Cognitive Workloads. Iran J Psychiatry. 2024;19(4):356-366.
