Dynamic functional connectivity correlates of mental workload

Ahonen V, Leino M, Lipping T (2021) Electroencephalography in evaluating mental workload of gaming. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE, pp 845–848

Appel T, Gerjets P, Hoffman S et al (2023) Cross-task and cross-participant classification of cognitive load in an emergency simulation game. IEEE Trans Affect Comput 14(2):1558–1571

Article  Google Scholar 

Arthur D, Vassilvitskii S (2007) K-means++ the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, pp 1027–1035

Borghini G, Astolfi L, Vecchiato G et al. (2014) Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev 44:58–75

Article  PubMed  Google Scholar 

Brouwer AM, Hogervorst MA, Van Erp JB et al. (2012) Estimating workload using EEG spectral power and ERPs in the n-back task. J Neural Eng 9(4):045008

Article  PubMed  Google Scholar 

Carlson S, Martinkauppi S, Rämä P et al. (1998) Distribution of cortical activation during visuospatial n-back tasks as revealed by functional magnetic resonance imaging. Cereb Cortex (New York, NY: 1991) 8(8):743–752

CAS  Google Scholar 

Chang C, Liu Z, Chen MC et al. (2013) EEG correlates of time-varying bold functional connectivity. Neuroimage 72:227–236

Article  PubMed  Google Scholar 

Dai Z, De Souza J, Lim J et al. (2017) Eeg cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands. Front Hum Neurosci 11:237

Article  PubMed  PubMed Central  Google Scholar 

Demir S, Türker İ (2021) Arithmetic success and gender-based characterization of brain connectivity across EEG bands. Biomed Signal Process Control 64(102):222

Google Scholar 

Dimitrakopoulos GN, Kakkos I, Dai Z et al. (2017) Task-independent mental workload classification based upon common multiband EEG cortical connectivity. IEEE Trans Neural Syst Rehabil Eng 25(11):1940–1949

Article  PubMed  Google Scholar 

Dimitrakopoulos GN, Kakkos I, Anastasiou A et al. (2023) Cognitive reorganization due to mental workload: A functional connectivity analysis based on working memory paradigms. Appl Sci 13(4):2129

Article  CAS  Google Scholar 

Dunn JC (1974) Well-separated clusters and optimal fuzzy partitions. J Cybern 4(1):95–104

Article  Google Scholar 

Dussault C, Jouanin JC, Philippe M et al. (2005) EEG and ECG changes during simulator operation reflect mental workload and vigilance. Aviat Space Environ Med 76(4):344–351

PubMed  Google Scholar 

Greicius MD, Krasnow B, Reiss AL et al. (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci 100(1):253–258

Article  CAS  PubMed  Google Scholar 

Guan K, Zhang Z, Chai X et al. (2022) Eeg based dynamic functional connectivity analysis in mental workload tasks with different types of information. IEEE Trans Neural Syst Rehabil Eng 30:632–642

Article  PubMed  Google Scholar 

Herrera-Díaz A, Mendoza-Quiñones R, Melie-Garcia L et al. (2016) Functional connectivity and quantitative EEG in women with alcohol use disorders: a resting-state study. Brain Topogr 29:368–381

Article  PubMed  Google Scholar 

Hoedemaeker M (2002) Summary description of workload indicators: Wp1 workload measures. Human machine interface and the safety of traffic in Europe growth project. Technical report, GRD1-2000-25361. HASTE. Institute for Transport Studies. Leeds, UK

Ismail LE, Karwowski W (2020) A graph theory-based modeling of functional brain connectivity based on EEG: a systematic review in the context of neuroergonomics. IEEE Access 8:155103–155135

Article  Google Scholar 

Jian W, Chen M, McFarland DJ (2017) EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement. Brain Res Bull 130:156–164

Article  PubMed  PubMed Central  Google Scholar 

Kakkos I, Dimitrakopoulos GN, Gao L et al. (2019) Mental workload drives different reorganizations of functional cortical connectivity between 2d and 3d simulated flight experiments. IEEE Trans Neural Syst Rehabil Eng 27(9):1704–1713

Article  PubMed  Google Scholar 

Kaposzta Z, Stylianou O, Mukli P et al. (2021) Decreased connection density and modularity of functional brain networks during n-back working memory paradigm. Brain Behav 11(1):e01932

Article  PubMed  Google Scholar 

Khanna A, Pascual-Leone A, Michel CM et al. (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 49:105–113

Article  PubMed  Google Scholar 

Langer N, Von Bastian CC, Wirz H et al. (2013) The effects of working memory training on functional brain network efficiency. Cortex 49(9):2424–2438

Article  PubMed  Google Scholar 

Lehmann D, Faber PL, Galderisi S et al. (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Res Neuroimaging 138(2):141–156

Article  Google Scholar 

Li D, Wang X, Menassa CC et al. (2020) In: Start-up creation (Second Edition), second, edition. Woodhead Publishing Series in Civil and Structural Engineering, Woodhead Publishing, pp 291–341

Li KW, Lu Y, Li N (2022) Subjective and objective assessments of mental workload for UAV operations. Work 72(1):291–301

Article  PubMed  Google Scholar 

Liu Z, Si L, Xu W et al. (2022) Characteristics of EEG microstate sequences during propofol-induced alterations of brain consciousness states. IEEE Trans Neural Syst Rehabil Eng 30:1631–1641

Article  PubMed  Google Scholar 

Mishra B, Tarai S, Ratre V et al (2023) Processing of attentional and emotional stimuli depends on retrospective response of foot pressure: conceptualizing neuron-cognitive distribution in human brain. Comput Biol Med 164:107186

Article  PubMed  Google Scholar 

Newman ME, Barabási ALE, Watts DJ (2006) The structure and dynamics of networks. Princeton University Press, Princeton

Google Scholar 

Núñez P, Poza J, Gómez C et al. (2021) Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum. Neuroimage 232(117):898

Google Scholar 

Oldham S, Fulcher B, Parkes L et al. (2019) Consistency and differences between centrality measures across distinct classes of networks. PloS One 14(7):e0220061

Article  CAS  PubMed  PubMed Central  Google Scholar 

Palva JM, Monto S, Kulashekhar S et al. (2010) Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proc Natl Acad Sci 107(16):7580–7585

Article  CAS  PubMed  PubMed Central  Google Scholar 

Popov T, Popova P, Harkotte M et al. (2018) Cross-frequency interactions between frontal theta and posterior alpha control mechanisms foster working memory. Neuroimage 181:728–733

Article  PubMed  Google Scholar 

Prasad R, Tarai S, Bit A (2022) Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions. Cogn Neurodyn 1–24

Raichle ME, MacLeod AM, Snyder AZ et al. (2001) A default mode of brain function. Proc Natl Acad Sci 98(2):676–682

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ren S, Li J, Taya F et al. (2016) Dynamic functional segregation and integration in human brain network during complex tasks. IEEE Trans Neural Syst Rehabil Eng 25(6):547–556

Article  Google Scholar 

Roy RN, Bonnet S, Charbonnier S et al. (2016) Efficient workload classification based on ignored auditory probes: a proof of concept. Front Hum Neurosci 10:519

Article  PubMed  PubMed Central  Google Scholar 

Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

Article  PubMed  Google Scholar 

Rubio S, Díaz E, Martín J et al. (2004) Evaluation of subjective mental workload: a comparison of swat, NASA-TLX, and workload profile methods. Appl Psychol 53(1):61–86

Article  Google Scholar 

Shaw JB, Weekley JA (1985) The effects of objective work-load variations of psychological strain and post-work-load performance. J Manag 11(1):87–98

Google Scholar 

Shi W, Li Y, Liu Z et al. (2020) Non-canonical microstate becomes salient in high density EEG during propofol-induced altered states of consciousness. Int J Neural Syst 30(02):2050005

Article  PubMed  Google Scholar 

So WK, Wong SW, Mak JN et al. (2017) An evaluation of mental workload with frontal EEG. PloS One 12(4):e0174949

Article  PubMed  PubMed Central  Google Scholar 

Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and meg with diminished bias from common sources. Hum Brain Mapp 28(11):1178–1193

Article  PubMed  PubMed Central  Google Scholar 

Tang S, Liu C, Zhang Q et al. (2021) Mental workload classification based on ignored auditory probes and spatial covariance. J Neural Eng 18(4):0460c9

Article  Google Scholar 

Tarai S, Qurratul QA, Ratre V et al. (2022) Neurocognitive functions of prosocial and unsocial incongruency information during language comprehension: evidence from time-frequency analysis of EEG signals. Med Biol Eng Comput 60(4):1033–1053

Article  PubMed  Google Scholar 

Tukey JW et al. (1977) Exploratory data analysis, vol 2. Reading, MA

Google Scholar 

Vidaurre C, Blankertz B (2010) Towards a cure for BCI illiteracy. Brain Topogr 23:194–198

Article  PubMed  Google Scholar 

Vidaurre D, Smith SM, Woolrich MW (2017) Brain network dynamics are hierarchically organized in time. Proc Natl Acad Sci 114(48):12827–12832

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vidaurre D, Hunt LT, Quinn AJ et al. (2018) Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nat Commun 9(1):2987

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