Adebayo, J., Gilmer, J., Muelly, M., et al. (2018). Sanity Checks for Saliency Maps. publication Title: ADS Bibcode: 2018arXiv181003292A arXiv1810.03292

Adhikari, S., Choudhury, N., Bhattacharya, S., et al. (2025). Analysis of frequency domain features for the classification of evoked emotions using EEG signals. Experimental Brain Research, 243(3), 65. https://doi.org/10.1007/s00221-025-07002-1

Article  PubMed  PubMed Central  Google Scholar 

Aggarwal, S., & Chugh, N. (2022). Review of Machine Learning Techniques for EEG Based Brain Computer Interface. Archives of Computational Methods in Engineering, 29(5), 3001–3020. https://doi.org/10.1007/s11831-021-09684-6

Article  Google Scholar 

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705

Article  Google Scholar 

Chiarion, G., Sparacino, L., Antonacci, Y., et al. (2023). Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends. Bioengineering, 10(3), 372. https://doi.org/10.3390/bioengineering10030372

Article  PubMed  PubMed Central  Google Scholar 

Craik, A., He, Y., & Contreras-Vidal, J. L. (2019). Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal of Neural Engineering,16(3), Article 031001. https://doi.org/10.1088/1741-2552/ab0ab5

Dissanayaka, C., Ben-Simon, E., Gruberger, M., et al. (2015). Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods. Medical & Biological Engineering & Computing, 53(7), 599–607. https://doi.org/10.1007/s11517-015-1272-0

Article  Google Scholar 

Duan, RN., Zhu, JY., Lu, BL. (2013). Differential entropy feature for EEG-based emotion classification. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 81–84, https://doi.org/10.1109/NER.2013.6695876, iSSN: 1948-3554

Ellis, C. A., Sancho, M. L., Miller, R. L., et al. (2024). Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. https://doi.org/10.1101/2024.03.19.585728

Article  Google Scholar 

Ermolova, M., Metsomaa, J., Zrenner, C., et al. (2021). Spontaneous phase-coupling within cortico-cortical networks: How time counts for brain-state-dependent stimulation. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 14(2), 404–406. https://doi.org/10.1016/j.brs.2021.02.007, publisher: Elsevier

Farahat, A., Reichert, C., Sweeney-Reed, C. M., et al. (2019). Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization. Journal of Neural Engineering,16(6), Article 066010. https://doi.org/10.1088/1741-2552/ab3bb4

Friston, K., Moran, R., & Seth, A. K. (2013). Analysing connectivity with Granger causality and dynamic causal modelling. Current Opinion in Neurobiology, 23(2), 172–178. https://doi.org/10.1016/j.conb.2012.11.010

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fu, Z., Du, Y., & Calhoun, V. D. (2019). The Dynamic Functional Network Connectivity Analysis Framework. Engineering (Beijing, China), 5(2), 190–193. https://doi.org/10.1016/j.eng.2018.10.001

Article  PubMed  Google Scholar 

Gemein, L. A., Schirrmeister, R. T., Chrabaszcz, P., et al. (2020). Machine-learning-based diagnostics of EEG pathology. NeuroImage,220, Article 117021. https://doi.org/10.1016/j.neuroimage.2020.117021

Geng, B., Liu, K., Duan, Y., et al. (2020). A Novel EEG Based Directed Transfer Function for Investigating Human Perception to Audio Noise. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 923–928, https://doi.org/10.1109/IWCMC48107.2020.9148468, iSSN: 2376-6506

Gjølbye, A., Lehn-Schiøler, W., Jónsdóttir, A., et al. (2024). Concept-based explainability for an EEG transformer model. ArXiv:2307.12745 [cs, eess, stat]

Goldberger, A. L., Amaral, L. A., Glass, L., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), E215-220. https://doi.org/10.1161/01.cir.101.23.e215

Article  CAS  PubMed  Google Scholar 

Hövel, P., Viol, A., Loske, P., et al. (2020). Synchronization in Functional Networks of the Human Brain. Journal of Nonlinear Science,30(5), 2259–2282. https://doi.org/10.1007/s00332-018-9505-7

Hussain, I., Jany, R., Boyer, R., et al. (2023). An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME. Sensors (Basel, Switzerland), 23(17), 7452. https://doi.org/10.3390/s23177452

Article  CAS  PubMed  Google Scholar 

Hutchison, R. M., Womelsdorf, T., Allen, E. A., et al. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079

Article  PubMed  Google Scholar 

Ieracitano, C., Mammone, N., Hussain, A., et al. (2022). A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Computing and Applications, 34(14), 11347–11360. https://doi.org/10.1007/s00521-020-05624-w

Article  Google Scholar 

Köllőd, C. M., Adolf, A., Iván, K., et al. (2023). Deep Comparisons of Neural Networks from the EEGNet Family. Electronics,12(12), 2743. https://doi.org/10.3390/electronics12122743

Lachaux, J., Rodriguez, E., Martinerie, J., et al. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping,8(4), 194–208. https://doi.org/10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C

Lawhern, V. J., Solon, A. J., Waytowich, N. R., et al. (2018). EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering,15(5), Article 056013. https://doi.org/10.1088/1741-2552/aace8c

Lopes, M., Cassani, R., & Falk, T. H. (2023). Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer’s Disease Diagnosis. Computational Intelligence and Neuroscience,2023, 3198066. https://doi.org/10.1155/2023/3198066

Lynn, C. W., & Bassett, D. S. (2019). The physics of brain network structure, function and control. Nature Reviews Physics, 1(5), 318–332. https://doi.org/10.1038/s42254-019-0040-8

Article  Google Scholar 

Mortier, S., Turkeš, R., De Winne, J., et al. (2023). Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography. Sensors, 23(23), 9588. https://doi.org/10.3390/s23239588

Article  PubMed  PubMed Central  Google Scholar 

Panwar, S., Joshi, S. D., Gupta, A., et al. (2021). Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG. Scientific Reports, 11(1), 2822. https://doi.org/10.1038/s41598-021-81884-3, publisher: Nature Publishing Group

Pester, B., Ligges, C. (2018). Does independent component analysis influence EEG connectivity analyses? In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Honolulu, HI, pp. 1007–1010, https://doi.org/10.1109/EMBC.2018.8512425

Phan, A. T., Xie, W., Chapeton, J. I., et al. (2024). Dynamic patterns of functional connectivity in the human brain underlie individual memory formation. Nature Communications, 15(1), 8969. https://doi.org/10.1038/s41467-024-52744-1, publisher: Nature Publishing Group

Prabowo, D. W., Nugroho, H. A., Setiawan, N. A., et al. (2023). A systematic literature review of emotion recognition using EEG signals. Cognitive Systems Research,82, Article 101152. https://doi.org/10.1016/j.cogsys.2023.101152

Roy, Y., Banville, H., Albuquerque, I., et al. (2019). Deep learning-based electroencephalography analysis: a systematic review. Journal of Neural Engineering,16(5), Article 051001. https://doi.org/10.1088/1741-2552/ab260c

Saeidi, M., Karwowski, W., Farahani, F. V., et al. (2021). Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sciences, 11(11), 1525. https://doi.org/10.3390/brainsci11111525

Article  PubMed  PubMed Central  Google Scholar 

Salami, A., Andreu-Perez, J., & Gillmeister, H. (2022). EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification. IEEE Access, 10, 36672–36685. https://doi.org/10.1109/ACCESS.2022.3161489

Article  Google Scholar 

Schalk, G., McFarland, DJ., Hinterberger, T., et al. (2009). EEG Motor Movement/Imagery Dataset. https://doi.org/10.13026/C28G6P

Schalk, G., McFarland, D., Hinterberger, T., et al. (2004). BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering, 51(6), 1034–1043. https://doi.org/10.1109/TBME.2004.827072

Article  PubMed  Google Scholar 

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., et al. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11), 5391–5420. https://doi.org/10.1002/hbm.23730

Seth, A. K., Barrett, A. B., & Barnett, L. (2015). Granger Causality Analysis in Neuroscience and Neuroimaging. Journal of Neuroscience, 35(8), 3293–3297. https://doi.org/10.1523/JNEUROSCI.4399-14.2015

Article  CAS  PubMed  Google Scholar 

Shaw, S. B., McKinnon, M. C., Heisz, J., et al. (2021). Dynamic task-linked switching between brain networks – A tri-network perspective. Brain and Cognition,151, Article 105725. https://doi.org/10.1016/j.bandc.2021.105725

Sujatha Ravindran, A., & Contreras-Vidal, J. (2023). An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth. Scientific Reports, 13, 17709. https://doi.org/10.1038/s41598-023-43871-8

Article  CAS  PubMed  PubMed Central  Google Scholar 

Šverko, Z., Vlahinić, S., & Rogelj, P. (2024). A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation. Algorithms, 17(11), 517. https://doi.org/10.3390/a17110517, number: 11 Publisher: Multidisciplinary Digital Publishing Institute

Šverko Z, Vrankić M, Vlahinić S, et al (2022a) Complex Pearson Correlation Coefficient for EEG Connectivity Analysis. Sensors (Basel, Switzerland) 22(4):1477. https://doi.org/10.3390/s22041477

Šverko Z, Vrankic M, Vlahinić S, et al (2022b) Dynamic Connectivity Analysis Using Adaptive Window Size. Sensors 22(14):5162. https://doi.org/10.3390/s22145162

Vinck, M., Oostenveld, R., van Wingerden, M., et al. (2011). An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage, 55(4), 1548–1565. https://doi.org/10.1016/j.neuroimage.2011.01.055

Article  PubMed  Google Scholar 

Wang, H., Yang, K., Zhang, J., et al. (2024). Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain

Wang, H., Zhu, X., Chen, T., et al. (2022). Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model. ArXiv:2205.14976 [cs, eess]

Wang, J., & Wang, M. (2021). Review of the emotional feature extraction and classification using EEG signals. Cognitive Robotics, 1, 29–40. https://doi.org/10.1016/j.cogr.2021.04.001

Article  Google Scholar 

Winterhalder, M., Schelter, B., Hesse, W., et al. (2005). Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems. Signal Processing, 85(11), 2137–2160. https://doi.org/10.1016/j.sigpro.2005.07.011

Article  Google Scholar 

Yang, G., & Liu, J. (2024). A novel multi-scale fusion convolutional neural network for EEG-based motor imagery classification. Biomedical Signal Processing and Control,96, Article 106645. https://doi.org/10.1016/j.bspc.2024.106645

Zhao, W., Jiang, X., Zhang, B., et al. (2024). CTNet: a convolutional transformer network for EEG-based motor imagery classification. Scientific reports, 14(1), 20237. https://doi.org/10.1038/s41598-024-71118-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zheng, W. L., & Lu, B. L. (2015). Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks. IEEE Transactions on Autonomous Mental Development, 7(3), 162–175. https://doi.org/10.1109/TAMD.2015.2431497, conference Name: IEEE Transactions on Autonomous Mental Development