Research on the relationship between resting-state spontaneous electroencephalography and task-evoked electroencephalography_Journal of Biomedical Engineering

Authors：

HE Huan ^1,2 ,  XIAO Xiaolin ^2,3 , YUE Jin ² , XU Minpeng ^2,3 , MING Dong ^2,3

1. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China;
2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China;
3. Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072, P. R. China;

Corresponding author：

XIAO Xiaolin, Email: xiaoxiao0@tju.edu.cn

Keywords：

Spontaneous electroencephalography; Task-evoked electroencephalography; Regulating effect; Dynamic impact

DOI：

10.7507/1001-5515.202501021

Video：

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Abstract Full text Figures/Tables Video References Cited by

In recent years, it has become a new direction in the field of neuroscience to explore the mode characteristics, functional significance and interaction mechanism of resting spontaneous electroencephalography (EEG) and task-evoked EEG. This paper introduced the basic characteristics of spontaneous EEG and task-evoked EEG, and summarized the core role of spontaneous EEG in shaping the adaptability of the nervous system. It focused on how the spontaneous EEG interacted with the task-evoked EEG in the process of task processing, and emphasized that the spontaneous EEG could significantly affect the performance of tasks such as perception, cognition and movement by regulating neural activities and predicting external stimuli. These studies provide an important theoretical basis for in-depth understanding of the principle and mechanism of brain information processing in resting and task states, and point out the direction for further exploring the complex relationship between them in the future.

Citation： HE Huan, XIAO Xiaolin, YUE Jin, XU Minpeng, MING Dong. Research on the relationship between resting-state spontaneous electroencephalography and task-evoked electroencephalography. Journal of Biomedical Engineering, 2025, 42(3): 620-627. doi: 10.7507/1001-5515.202501021 Copy

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2.	Janiukstyte V, Owen T W, Chaudhary U J, et al. Normative brain mapping using scalp EEG and potential clinical application. Sci Rep, 2023, 13(1): 13442.
3.	Capilla A, Arana L, García-Huéscar M, et al. The natural frequencies of the resting human brain: An MEG-based atlas. NeuroImage, 2022, 258: 119373.
4.	Chen W L, Wagner J, Heugel N, et al. Functional near-infrared spectroscopy and its clinical application in the field of neuroscience: Advances and future directions. Front Neurosci, 2020, 14: 724.
5.	Sun L, Zhao T, Liang X, et al. Human lifespan changes in the brain’s functional connectome. Nat Neurosci, 2025, 28(4): 891-901.
6.	Lucas-Romero J, Rivera-Arconada I, Lopez-Garcia J A. Noise or signal? Spontaneous activity of dorsal horn neurons: patterns and function in health and disease. Pflug Arch Eur J Phy, 2024, 476(8): 1171-1186.
7.	Benhamou J, Quyen M L V, Marrelec G. Time-frequency analysis of event-related brain recordings: Connecting power of evoked potential and inter-trial coherence. IEEE T Bio-Med Eng, 2023, 70(5): 1599-1610.
8.	Uddin L Q. Bring the Noise: Reconceptualizing spontaneous neural activity. Trends Cogn Sci, 2020, 24(9): 734-746.
9.	Pezzulo G, Zorzi M, Corbetta M. The secret life of predictive brains: What’s spontaneous activity for?. Trends Cogn Sci, 2021, 25(9): 730-743.
10.	Sarracino A, Arviv O, Shriki O, et al. Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity. Phys Rev Res, 2020, 2(3): 033355.
11.	Wainio-Theberge S, Wolff A, Northoff G. Dynamic relationships between spontaneous and evoked electrophysiological activity. Commun Biol, 2021, 4(1): 741.
12.	Myrov V, Siebenhühner F, Juvonen J J, et al. Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture. Commun Biol, 2024, 7(1): 405.
13.	Mahjoory K, Schoffelen J M, Keitel A, et al. The frequency gradient of human resting-state brain oscillations follows cortical hierarchies. Elife, 2020, 9: e53715.
14.	Takahashi K, Glinski B, Salehinejad M A, et al. Induction and stabilization of delta frequency brain oscillations by phase-synchronized rTMS and tACS. Brain Stimul, 2024, 17(5): 1086-1097.
15.	Moreau Q, Parrotta E, Era V, et al. Role of the occipito-temporal theta rhythm in hand visual identification. J Neurophysiol, 2020, 123(1): 167-177.
16.	Pusil S, Zegarra-Valdivia J, Cuesta P, et al. Effects of spaceflight on the EEG alpha power and functional connectivity. Sci Rep, 2023, 13(1): 9489.
17.	Peng J, Zikereya T, Shao Z, et al. The neuromechanical of beta-band corticomuscular coupling within the human motor system. Front Neurosci, 2024, 18: 1441002.
18.	Ichim A M, Barzan H, Moca V V, et al. The gamma rhythm as a guardian of brain health. Elife, 2024, 13: e100238.
19.	Trevino G, Lee J J, Shimony J S, et al. Complexity organization of resting-state functional-MRI networks. Hum Brain Mapp, 2024, 45(12): e26809.
20.	Yeshurun Y, Nguyen M, Hasson U. The default mode network: where the idiosyncratic self meets the shared social world. Nat Rev Neurosci, 2021, 22(3): 181-192.
21.	Samogin J, Marino M, Porcaro C, et al. Frequency-dependent functional connectivity in resting state networks. Hum Brain Mapp, 2020, 41(18): 5187-5198.
22.	Korn U, Krylova M, Heck K L, et al. EEG-microstates reflect auditory distraction after attentive audiovisual perception recruitment of cognitive control networks. Front Syst Neurosci, 2021, 15: 751226.
23.	Kinney-Lang E, Yoong M, Hunter M, et al. Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy. Epilepsy Behav, 2019, 90: 45-56.
24.	Al-Nuaimi A H, Blūma M, Al-Juboori S S, et al. Robust EEG based biomarkers to detect alzheimer’s disease. Brain Sci, 2021, 11(8): 1026.
25.	Krug S, Müller T, Kayali Ö, et al. Altered functional connectivity in common resting-state networks in patients with major depressive disorder: a resting-state functional connectivity study. J Psychiatr Res, 2022, 155: 33-41.
26.	Colombo M A, Comanducci A, Casarotto S, et al. Beyond alpha power: EEG spatial and spectral gradients robustly stratify disorders of consciousness. Cereb Cortex, 2023, 33(11): 7193-7210.
27.	Liu Y, Zeng W, Pan N, et al. EEG complexity correlates with residual consciousness level of disorders of consciousness. BMC Neuro, 2023, 23(1): 140.
28.	Lewandowska M, Tołpa K, Rogala J, et al. Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: The spatial distribution and sex/gender-related differences. Behav Brain Funct, 2023, 19(1): 18.
29.	Donoghue T, Voytek B. Automated meta-analysis of the event-related potential (ERP) literature. Sci Rep, 2022, 12(1): 1867.
30.	Xu M, Jia Y, Qi H, et al. Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin. NeuroImage, 2016, 134: 204-212.
31.	Fiorini L, Berchicci M, Mussini E, et al. Neural basis of anticipatory multisensory integration. Brain Sci, 2021, 11(7): 843.
32.	Menon V, D’Esposito M. The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology, 2021, 47(1): 90-103.
33.	Kong X, Kong R, Orban C, et al. Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nat Commun, 2021, 12(1): 6373.
34.	Stieger J R, Engel S A, He B. Continuous sensorimotor rhythm based brain computer interface learning in a large population. Sci Data, 2021, 8(1): 98.
35.	Faust T E, Gunner G, Schafer D P. Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS. Nat Rev Neurosci, 2021, 22(11): 657-673.
36.	Avitan L, Pujic Z, Mölter J, et al. Spontaneous and evoked activity patterns diverge over development. Elife, 2021, 10: e61942.
37.	Newbold D J, Laumann T O, Hoyt C R, et al. Plasticity and spontaneous activity pulses in disused human brain circuits. Neuron, 2020, 107(3): 580-589.e6.
38.	Yu Q, Fu H, Wang G, et al. Short-term visual experience leads to potentiation of spontaneous activity in mouse superior colliculus. Neurosci Bull, 2021, 37(3): 353-368.
39.	Albertson A J, Landsness E C, Tang M J, et al. Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity. NeuroImage, 2022, 257: 119287.
40.	Yoshimura N, Tsuda H, Aquino D, et al. Age-related decline of sensorimotor integration influences resting-state functional brain connectivity. Brain Sci, 2020, 10(12): 966.
41.	Won J, Nielson K A, Smith J C. Large-scale network connectivity and cognitive function changes after exercise training in older adults with intact cognition and mild cognitive impairment. J Alzheimers Dis Rep, 2023, 7(1): 399-413.
42.	Cui X, Gui W, Miao J, et al. A combined intervention of aerobic exercise and video game in older adults: The efficacy and neural basis on improving mnemonic discrimination. J Gerontol A Biol Sci Med Sci, 2022, 78(8): 1436-1444.
43.	Iemi L, Busch N A, Laudini A, et al. Multiple mechanisms link prestimulus neural oscillations to sensory responses. Elife, 2019, 8: e43620.
44.	Krasich K, Simmons C, O’Neill K, et al. Prestimulus oscillatory brain activity interacts with evoked recurrent processing to facilitate conscious visual perception. Sci Rep, 2022, 12(1): 22126.
45.	Zhou Y J, Iemi L, Schoffelen J M, et al. Alpha oscillations shape sensory representation and perceptual sensitivity. J Neurosci, 2021, 41(46): 9581-9592.
46.	Benwell C S Y, Coldea A, Harvey M, et al. Low pre-stimulus EEG alpha power amplifies visual awareness but not visual sensitivity. Eur J Neurosci, 2021, 55(11-12): 3125-3140.
47.	Jiang Z, An X, Liu S, et al. Beyond alpha band: Prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus. J Neural Eng, 2024, 21(3): 036035.
48.	Darch H T, Cerminara N L, Gilchrist I D, et al. Pre-movement changes in sensorimotor beta oscillations predict motor adaptation drive. Sci Rep, 2020, 10(1): 17946.
49.	Tan E, Troller-Renfree S V, Morales S, et al. Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research. Dev Cogn Neurosci, 2024, 67: 101404.
50.	Parto-Dezfouli M, Vezoli J, Bosman C A, et al. Enhanced behavioral performance through interareal gamma and beta synchronization. Cell Rep, 2023, 42(10): 113249.
51.	Sirpal P, Damseh R, Peng K, et al. Multimodal autoencoder predicts fNIRS resting state from EEG signals. Neuroinformatics, 2021, 20(3): 537-558.
52.	Zhang C, Wang Y, Jing X, et al. Brain mechanisms of mental processing: from evoked and spontaneous brain activities to enactive brain activity. Psychoradiology, 2023, 3: kkad010.
53.	Fong A H C, Yoo K, Rosenberg M D, et al. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. NeuroImage, 2019, 188: 14-25.
54.	Patil A U, Ghate S, Madathil D, et al. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep, 2021, 11(1): 165.
55.	Nickerson L D. Replication of resting state-task network correspondence and novel findings on brain network activation during task fMRI in the human connectome project study. Sci Rep, 2018, 8(1): 17543.
56.	Zhang J, Huang Z, Tumati S, et al. Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. Plos Biol, 2020, 18(7): e3000733.
57.	Chen J, Ke Y, Ni G, et al. Evidence for modulation of EEG microstates by mental workload levels and task types. Hum Brain Mapp, 2023, 45(1): e26552.
58.	Wan W, Gao Z, Zhang Q, et al. Resting state EEG complexity as a predictor of cognitive performance. Physica A, 2023, 624: 128952.
59.	Phadikar S, Pusuluri K, Iraji A, et al. Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity. Front Neurosci, 2025, 19: 1484954.
60.	Gholamipourbarogh N, Vahid A, Mückschel M, et al. Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition. Psychophysiology, 2023, 60(10): e14328.
61.	Osher D E, Brissenden J A, Somers D C. Predicting an individual’s dorsal attention network activity from functional connectivity fingerprints. J Neurophysiol, 2019, 122(1): 232-240.
62.	Cohen A D, Chen Z, Parker Jones O, et al. Regression-based machine-learning approaches to predict task activation using resting-state fMRI. Hum Brain Mapp, 2019, 41(3): 815-826.
63.	Lacosse E, Scheffler K, Lohmann G, et al. Jumping over baselines with new methods to predict activation maps from resting-state fMRI. Sci Rep, 2021, 11(1): 3480.
64.	Lin L, Chang D, Song D, et al. Lower resting brain entropy is associated with stronger task activation and deactivation. NeuroImage, 2022, 249: 118875.
65.	Afrashteh N, Inayat S, Bermudez-Contreras E, et al. Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice. Cell Rep, 2021, 37(10): 110081.
66.	Liu M, Liang Y, Song C, et al. Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice. Cell Rep, 2022, 41(10): 111740.
67.	Liu Y, Nour M M, Schuck N W, et al. Decoding cognition from spontaneous neural activity. Nat Rev Neurosci, 2022, 23(4): 204-214.

1. Annen J, Frasso G, van der Lande G J M, et al. Cerebral electrometabolic coupling in disordered and normal states of consciousness. Cell Rep, 2023, 42(8): 112854.
2. Janiukstyte V, Owen T W, Chaudhary U J, et al. Normative brain mapping using scalp EEG and potential clinical application. Sci Rep, 2023, 13(1): 13442.
3. Capilla A, Arana L, García-Huéscar M, et al. The natural frequencies of the resting human brain: An MEG-based atlas. NeuroImage, 2022, 258: 119373.
4. Chen W L, Wagner J, Heugel N, et al. Functional near-infrared spectroscopy and its clinical application in the field of neuroscience: Advances and future directions. Front Neurosci, 2020, 14: 724.
5. Sun L, Zhao T, Liang X, et al. Human lifespan changes in the brain’s functional connectome. Nat Neurosci, 2025, 28(4): 891-901.
6. Lucas-Romero J, Rivera-Arconada I, Lopez-Garcia J A. Noise or signal? Spontaneous activity of dorsal horn neurons: patterns and function in health and disease. Pflug Arch Eur J Phy, 2024, 476(8): 1171-1186.
7. Benhamou J, Quyen M L V, Marrelec G. Time-frequency analysis of event-related brain recordings: Connecting power of evoked potential and inter-trial coherence. IEEE T Bio-Med Eng, 2023, 70(5): 1599-1610.
8. Uddin L Q. Bring the Noise: Reconceptualizing spontaneous neural activity. Trends Cogn Sci, 2020, 24(9): 734-746.
9. Pezzulo G, Zorzi M, Corbetta M. The secret life of predictive brains: What’s spontaneous activity for?. Trends Cogn Sci, 2021, 25(9): 730-743.
10. Sarracino A, Arviv O, Shriki O, et al. Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity. Phys Rev Res, 2020, 2(3): 033355.
11. Wainio-Theberge S, Wolff A, Northoff G. Dynamic relationships between spontaneous and evoked electrophysiological activity. Commun Biol, 2021, 4(1): 741.
12. Myrov V, Siebenhühner F, Juvonen J J, et al. Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture. Commun Biol, 2024, 7(1): 405.
13. Mahjoory K, Schoffelen J M, Keitel A, et al. The frequency gradient of human resting-state brain oscillations follows cortical hierarchies. Elife, 2020, 9: e53715.
14. Takahashi K, Glinski B, Salehinejad M A, et al. Induction and stabilization of delta frequency brain oscillations by phase-synchronized rTMS and tACS. Brain Stimul, 2024, 17(5): 1086-1097.
15. Moreau Q, Parrotta E, Era V, et al. Role of the occipito-temporal theta rhythm in hand visual identification. J Neurophysiol, 2020, 123(1): 167-177.
16. Pusil S, Zegarra-Valdivia J, Cuesta P, et al. Effects of spaceflight on the EEG alpha power and functional connectivity. Sci Rep, 2023, 13(1): 9489.
17. Peng J, Zikereya T, Shao Z, et al. The neuromechanical of beta-band corticomuscular coupling within the human motor system. Front Neurosci, 2024, 18: 1441002.
18. Ichim A M, Barzan H, Moca V V, et al. The gamma rhythm as a guardian of brain health. Elife, 2024, 13: e100238.
19. Trevino G, Lee J J, Shimony J S, et al. Complexity organization of resting-state functional-MRI networks. Hum Brain Mapp, 2024, 45(12): e26809.
20. Yeshurun Y, Nguyen M, Hasson U. The default mode network: where the idiosyncratic self meets the shared social world. Nat Rev Neurosci, 2021, 22(3): 181-192.
21. Samogin J, Marino M, Porcaro C, et al. Frequency-dependent functional connectivity in resting state networks. Hum Brain Mapp, 2020, 41(18): 5187-5198.
22. Korn U, Krylova M, Heck K L, et al. EEG-microstates reflect auditory distraction after attentive audiovisual perception recruitment of cognitive control networks. Front Syst Neurosci, 2021, 15: 751226.
23. Kinney-Lang E, Yoong M, Hunter M, et al. Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy. Epilepsy Behav, 2019, 90: 45-56.
24. Al-Nuaimi A H, Blūma M, Al-Juboori S S, et al. Robust EEG based biomarkers to detect alzheimer’s disease. Brain Sci, 2021, 11(8): 1026.
25. Krug S, Müller T, Kayali Ö, et al. Altered functional connectivity in common resting-state networks in patients with major depressive disorder: a resting-state functional connectivity study. J Psychiatr Res, 2022, 155: 33-41.
26. Colombo M A, Comanducci A, Casarotto S, et al. Beyond alpha power: EEG spatial and spectral gradients robustly stratify disorders of consciousness. Cereb Cortex, 2023, 33(11): 7193-7210.
27. Liu Y, Zeng W, Pan N, et al. EEG complexity correlates with residual consciousness level of disorders of consciousness. BMC Neuro, 2023, 23(1): 140.
28. Lewandowska M, Tołpa K, Rogala J, et al. Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: The spatial distribution and sex/gender-related differences. Behav Brain Funct, 2023, 19(1): 18.
29. Donoghue T, Voytek B. Automated meta-analysis of the event-related potential (ERP) literature. Sci Rep, 2022, 12(1): 1867.
30. Xu M, Jia Y, Qi H, et al. Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin. NeuroImage, 2016, 134: 204-212.
31. Fiorini L, Berchicci M, Mussini E, et al. Neural basis of anticipatory multisensory integration. Brain Sci, 2021, 11(7): 843.
32. Menon V, D’Esposito M. The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology, 2021, 47(1): 90-103.
33. Kong X, Kong R, Orban C, et al. Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nat Commun, 2021, 12(1): 6373.
34. Stieger J R, Engel S A, He B. Continuous sensorimotor rhythm based brain computer interface learning in a large population. Sci Data, 2021, 8(1): 98.
35. Faust T E, Gunner G, Schafer D P. Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS. Nat Rev Neurosci, 2021, 22(11): 657-673.
36. Avitan L, Pujic Z, Mölter J, et al. Spontaneous and evoked activity patterns diverge over development. Elife, 2021, 10: e61942.
37. Newbold D J, Laumann T O, Hoyt C R, et al. Plasticity and spontaneous activity pulses in disused human brain circuits. Neuron, 2020, 107(3): 580-589.e6.
38. Yu Q, Fu H, Wang G, et al. Short-term visual experience leads to potentiation of spontaneous activity in mouse superior colliculus. Neurosci Bull, 2021, 37(3): 353-368.
39. Albertson A J, Landsness E C, Tang M J, et al. Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity. NeuroImage, 2022, 257: 119287.
40. Yoshimura N, Tsuda H, Aquino D, et al. Age-related decline of sensorimotor integration influences resting-state functional brain connectivity. Brain Sci, 2020, 10(12): 966.
41. Won J, Nielson K A, Smith J C. Large-scale network connectivity and cognitive function changes after exercise training in older adults with intact cognition and mild cognitive impairment. J Alzheimers Dis Rep, 2023, 7(1): 399-413.
42. Cui X, Gui W, Miao J, et al. A combined intervention of aerobic exercise and video game in older adults: The efficacy and neural basis on improving mnemonic discrimination. J Gerontol A Biol Sci Med Sci, 2022, 78(8): 1436-1444.
43. Iemi L, Busch N A, Laudini A, et al. Multiple mechanisms link prestimulus neural oscillations to sensory responses. Elife, 2019, 8: e43620.
44. Krasich K, Simmons C, O’Neill K, et al. Prestimulus oscillatory brain activity interacts with evoked recurrent processing to facilitate conscious visual perception. Sci Rep, 2022, 12(1): 22126.
45. Zhou Y J, Iemi L, Schoffelen J M, et al. Alpha oscillations shape sensory representation and perceptual sensitivity. J Neurosci, 2021, 41(46): 9581-9592.
46. Benwell C S Y, Coldea A, Harvey M, et al. Low pre-stimulus EEG alpha power amplifies visual awareness but not visual sensitivity. Eur J Neurosci, 2021, 55(11-12): 3125-3140.
47. Jiang Z, An X, Liu S, et al. Beyond alpha band: Prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus. J Neural Eng, 2024, 21(3): 036035.
48. Darch H T, Cerminara N L, Gilchrist I D, et al. Pre-movement changes in sensorimotor beta oscillations predict motor adaptation drive. Sci Rep, 2020, 10(1): 17946.
49. Tan E, Troller-Renfree S V, Morales S, et al. Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research. Dev Cogn Neurosci, 2024, 67: 101404.
50. Parto-Dezfouli M, Vezoli J, Bosman C A, et al. Enhanced behavioral performance through interareal gamma and beta synchronization. Cell Rep, 2023, 42(10): 113249.
51. Sirpal P, Damseh R, Peng K, et al. Multimodal autoencoder predicts fNIRS resting state from EEG signals. Neuroinformatics, 2021, 20(3): 537-558.
52. Zhang C, Wang Y, Jing X, et al. Brain mechanisms of mental processing: from evoked and spontaneous brain activities to enactive brain activity. Psychoradiology, 2023, 3: kkad010.
53. Fong A H C, Yoo K, Rosenberg M D, et al. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. NeuroImage, 2019, 188: 14-25.
54. Patil A U, Ghate S, Madathil D, et al. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep, 2021, 11(1): 165.
55. Nickerson L D. Replication of resting state-task network correspondence and novel findings on brain network activation during task fMRI in the human connectome project study. Sci Rep, 2018, 8(1): 17543.
56. Zhang J, Huang Z, Tumati S, et al. Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. Plos Biol, 2020, 18(7): e3000733.
57. Chen J, Ke Y, Ni G, et al. Evidence for modulation of EEG microstates by mental workload levels and task types. Hum Brain Mapp, 2023, 45(1): e26552.
58. Wan W, Gao Z, Zhang Q, et al. Resting state EEG complexity as a predictor of cognitive performance. Physica A, 2023, 624: 128952.
59. Phadikar S, Pusuluri K, Iraji A, et al. Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity. Front Neurosci, 2025, 19: 1484954.
60. Gholamipourbarogh N, Vahid A, Mückschel M, et al. Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition. Psychophysiology, 2023, 60(10): e14328.
61. Osher D E, Brissenden J A, Somers D C. Predicting an individual’s dorsal attention network activity from functional connectivity fingerprints. J Neurophysiol, 2019, 122(1): 232-240.
62. Cohen A D, Chen Z, Parker Jones O, et al. Regression-based machine-learning approaches to predict task activation using resting-state fMRI. Hum Brain Mapp, 2019, 41(3): 815-826.
63. Lacosse E, Scheffler K, Lohmann G, et al. Jumping over baselines with new methods to predict activation maps from resting-state fMRI. Sci Rep, 2021, 11(1): 3480.
64. Lin L, Chang D, Song D, et al. Lower resting brain entropy is associated with stronger task activation and deactivation. NeuroImage, 2022, 249: 118875.
65. Afrashteh N, Inayat S, Bermudez-Contreras E, et al. Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice. Cell Rep, 2021, 37(10): 110081.
66. Liu M, Liang Y, Song C, et al. Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice. Cell Rep, 2022, 41(10): 111740.
67. Liu Y, Nour M M, Schuck N W, et al. Decoding cognition from spontaneous neural activity. Nat Rev Neurosci, 2022, 23(4): 204-214.

Journal of Biomedical Engineering

Research on the relationship between resting-state spontaneous electroencephalography and task-evoked electroencephalography

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