Abstract
Electroencephalography (EEG) is a widely used non-invasive technique to measure multi-channel potentials that reflect the electrical activity of the brain. Over the last few decades, EEG analysis has been an intensively explored research topic due to its potentials in being applied to the diagnosis of neurological diseases, such as epilepsy, brain tumors, head injury, sleep disorders, and dementia [19]. Despite many advances made in recent years, EEG signal analysis remains a challenging task. In addition to being non-stationary, EEG signals often have high noise-to-information ratios, and they can be significantly affected by various artifacts, demonstrating characteristics that differ from signals generated by activities in the brain [21]. Common artifacts include eye movements, jaw tension, and muscle contractions. To make effective signal analysis even more challenging, EEG signals are highly individual-specific, and cross-subject pattern identification can be elusive.
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Baltazar CA, Machado BS, de Faria DD, Paulo AJM, Silva SMCA, Ferraz HB, de Carvalho Aguiar P (2020) Brain connectivity in patients with dystonia during motor tasks. J Neural Eng 17(5):056039
Beste C, Heil M, Konrad C (2010) Individual differences in erps during mental rotation of characters: lateralization, and performance level. Brain Cogn 72(2):238–243
Carrier J, Land S, Buysse DJ, Kupfer DJ, Monk TH (2001) The effects of age and sex on sleep eeg power spectral density in the middle years of life (ages 20–60 years old). Psychophysiology 38(2):232–42
Clarke AR, Barry RJ, McCarthy R, Selikowitz M (2001) Age and sex effects in the eeg: development of the normal child. Clin Neurophysiol 112(5):806–814
Davidson RJ, Schwartz GE, Pugash E, Bromfield E (1976) Sex differences in patterns of eeg asymmetry. Biol Psychol 4(2):119–137
Gramfort A, Luessi M, Larson E, Engemann D, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, Hämäläinen M (2013) Meg and eeg data analysis with mne-python. Front Neurosci 7:267
Hirnstein M, Hugdahl K, Hausmann M (2019) Cognitive sex differences and hemispheric asymmetry: a critical review of 40 years of research. Laterality 24(2):204–252 PMID: 29985109
Höller Y, Bergmann J, Thomschewski A, Kronbichler M et al (2013) Comparison of eeg-features and classification methods for motor imagery in patients with disorders of consciousness. PLoS One 8(11):e80479
Ingalhalikar M, Smith A, Parker D, Satterthwaite TD, Elliott MA, Ruparel K, Hakonarson H, Gur RE, Gur RC, Verma R (2014) Sex differences in the structural connectome of the human brain. Proc Natl Acad Sci 111(2):823–828
La Rocca D, Campisi P, Végső B, Cserti P, Kozmann G, Babiloni F, De Vico Fallani F (2014) Human brain distinctiveness based on eeg spectral coherence connectivity. IEEE Trans Bio-med Eng 61:2406–2412
Li J, Deng JD, Ridder DD, Adhia D (2020) Gender classification of EEG signals using a motif attribute classification ensemble. In: 2020 international joint conference on neural networks, IJCNN 2020, Glasgow, United Kingdom, July 19–24, 2020. IEEE, pp 1–8
Lotte F, Congedo M, Lécuyer A, Fabrice L, Arnaldi B (2007) A review of classification algorithms for eeg-based brain-computer interfaces. J Neural Eng 4:07
Markovic A, Kaess M, Tarokh L (2020) Gender differences in adolescent sleep neurophysiology: a high-density sleep eeg study. Sci Reports 10:15935
Mumtaz W, Ali SS, Yasin MAM, Malik A (2017) A machine learning framework involving eeg-based functional connectivity to diagnose major depressive disorder (mdd). Med Biol Eng 56:233–246
Murias M, Webb SJ, Greenson J, Dawson G (2007) Resting state cortical connectivity reflected in eeg coherence in individuals with autism. Biol Psychiatry 62(3):270–273. Mechanisms of Circuit Dysfunction in Neurodevelopmental Disorders
Papousek I, Murhammer D, Schulter G (2011) Intra- and interindividual differences in lateralized cognitive performance and asymmetrical EEG activity in the frontal cortex. Brain Cogn 75(3):225–231
Scott WC, Kaiser D, Othmer S, Sideroff SI (2005) Effects of an eeg biofeedback protocol on a mixed substance abusing population. Am J Drug Alcohol Abuse 31(3):455–69
Shin Y, Lee S, Ahn M, Cho H, Jun SC, Lee H-N (2015) Noise robustness analysis of sparse representation based classification method for non-stationary eeg signal classification. Biomed Signal Process Control 21:8–8
Puthankattil Subha D, Joseph PK, Rajendra AU, Lim CM (2010) Eeg signal analysis: a survey. J Med Syst 34(2):195–212
van Putten M, Olbrich S, Arns M (2018) Predicting sex from brain rhythms with deep learning. Sci Rep 8:12
Vanneste S, Song J, De Ridder D (2018) Thalamocortical dysrhythmia detected by machine learning. Nature Commun 9:1103
Zhang J, Xu H, Zhu L, Kong W, Ma Z (2019) Gender recognition in emotion perception using eeg features. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 2883–2887
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Li, J., Deng, J.D., Adhia, D., De Ridder, D. (2021). Resting-State EEG Sex Classification Using Selected Brain Connectivity Representation. In: Pham, T.D., Yan, H., Ashraf, M.W., Sjöberg, F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-69951-2_13
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