Neuroinformatics and Artificial Intelligence

Publications since 2020

Published  | Accepted | Submitted 

Published:

  2024

• 200 p Q1 • Gabor, M, T Piotrowski, RLG Cavalcante, Positive concave deep equilibrium models, Proceedings of the 41st International Conference on Machine Learning, 21-27 July 2024, Vienna, Austria / S. Ruslan [et al. (ed.), 235, 2024.
• 200 p Q1 • He, X., Allison, B. Z., Qin, K., Liang, W., Wang, X., Cichocki, A., & Jin, J. (2024). Leveraging Transfer Superposition Theory for Stable-State Visual Evoked Potential Cross-Subject Frequency Recognition. IEEE Transactions on Biomedical Engineering, 71(11), 3071–3084. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2024.3406603
• 200 p Q1 • Jiang, X., Xu, X., Zhu, L., Sun, Z., Cichocki, A., & Shen, H. T. (2024). Resisting Noise in Pseudo Labels: Audible Video Event Parsing With Evidential Learning. IEEE Transactions on Neural Networks and Learning Systems, 1–15.
  https://doi.org/10.1109/TNNLS.2024.3505674
• 200 p Q1 • Jin, J., He, X., Xu, R., Zhao, R., Long, X., Wang, X., & Cichocki, A. (2024). Cross Stimulus Transfer Learning Framework Using Common Period Repetition Components for Fast Calibration of SSVEP Based BCIs.
  IEEE Internet of Things Journal, 1–1.
  https://doi.org/10.1109/JIOT.2024.3488957
• 200 p Q1 • Jin, J., Xu, R., Daly, I., Zhao, X., Wang, X., & Cichocki, A. (2024). MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces. IEEE Transactions on Cybernetics, 54(9), 5565–5576. https://doi.org/10.1109/TCYB.2024.3390805
• 200 p Q1 • Jin, J., Zhao, X., Daly, I., Li, S., Wang, X., Cichocki, A., & Jung, T.-P. (2024). A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-related Potentials.
  IEEE Transactions on Biomedical Engineering, 1–12. I https://doi.org/10.1109/TBME.2024.3492506
• 200 p Q1 • Leitao, A., Lucas, M., Poetto, S., Hersh, T. A., Gero, S., Gruber, D. F., Bronstein, M., & Petri, G. (2024). Evidence of social learning across symbolic cultural barriers in sperm whales. eLife, 13. https://doi.org/10.7554/eLife.96362.1
• 140 p Q1 • Adamska, I., & Finc, K. (11/2023). Effect of LSD and music on the time-varying brain dynamics. Psychopharmacology. IF = 4.4,
  Link: https://doi.org/10.1007/s00213-023-06394-8 
• 140 p Q1 • Cao, W., Chen, X., Yan, S., Zhou, Z., & Cichocki, A. (2024). 1-Bit Tensor Completion via Max-and-Nuclear Norm Composite Optimization. IEEE Transactions on Signal Processing, 72, 3487–3501. IEEE Transactions on Signal Processing. https://doi.org/10.1109/TSP.2024.3427136
• 140 p • Duch, W., Tołpa, K., Ratajczak, E., Hajnowski, M., Furman, Ł., Alexandre, L.A. (2024). Asymptotic Spatiotemporal Averaging of the Power of EEG Signals for Schizophrenia Diagnostics. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. Communications in Computer and Information Science, vol 1963, pp. 428-439. Springer, Singapore.  https://doi.org/10.1007/978-981-99-8138-0_34
• 140 p • Jankowski N, Dudek G, Automatic kernel construction during the neural network learning by modified fast singular value decomposition. International Conference on Computational Science, 2024 (submitted), In: Computational Science – ICCS 2024. Ed. by Leonardo Franco et al. Cham: Springer Nature Switzerland, 2024, pp. 205–212. isbn: 978-3-031-63759-9. doi: 10.1007/978-3-031-63759-9_25.
• 140 p Q1 • Jin, J., Bai, G., Xu, R., Qin, K., Sun, H., Wang, X., & Cichocki, A. (2024). A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces. Journal of Neural Engineering, 21(3), 036057. https://doi.org/10.1088/1741-2552/ad593b
• 140 p Q1 • Jin, J., Chen, W., Xu, R., Liang, W., Wu, X., He, X., Wang, X., & Cichocki, A. (2024). Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces. IEEE Journal of Biomedical and Health Informatics, 1–12. https://doi.org/10.1109/JBHI.2024.3472097
• 140 p Q1 • Piotrowski T, RLG Cavalcante, M Gabor, Fixed points of nonnegative neural networks,
  Journal of Machine Learning Research, 25(139), 2024.
• 140 p Q1 • Qin, K., Xu, R., Li, S., Wang, X., Cichocki, A., & Jin, J. (2024). A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, 1596–1605. https://doi.org/10.1109/TNSRE.2024.3386763
• 140 p Q1 • Wu, R., Jin, J., Daly, I., Wang, X., & Cichocki, A. (2023). Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 3075–3085.
  https://doi.org/10.1109/TNSRE.2023.3294815
• 140 p Q1 • Yu, Y., Bezerianos, A., Cichocki, A., & Li, J. (2023). Latent Space Coding Capsule Network for Mental Workload Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 3417–3427. https://doi.org/10.1109/TNSRE.2023.3307481
• 100 p Q1 • Qin, K., Xu, R., He, X., Zhang, Y., Wang, X., Cichocki, A., & Jin, J. (2024). A Time-Local Transformation Steady-State Visual Evoked Potentials Recognition Framework Combined With Filter Banks. IEEE Transactions on Instrumentation and Measurement, 73, 1–12.
  https://doi.org/10.1109/TIM.2024.3476551
• 100 p Q1 • Czekaj Ł, Kowalewski M, Domaszewicz J, Kitłowski R, Szwoch M, Duch W. (2024) A real-time sensor-based human activity recognition for eFitness and eHealth platforms. Sensors, 24(12), 3891. pp. 1-11.
• 100 p Q1 • Piotrowski T, R Ismayilov, M Frey, RLG Cavalcante, Inverse Feasibility in Over-the-Air Federated Learning, IEEE Signal Processing Letters, 31, 2024.
• 100 p Q1 • Qin, K., Xu, R., He, X., Zhang, Y., Wang, X., Cichocki, A., & Jin, J. (2024). A Time-Local Transformation Steady-State Visual Evoked Potentials Recognition Framework Combined With Filter Banks. IEEE Transactions on Instrumentation and Measurement, 73, 1–12.
  https://doi.org/10.1109/TIM.2024.3476551
• 100 p • Duch W. (2024), Artificial intelligence and the limits of the humanities. Er(r)go. Teoria-Literatura-Kultura 48(1), 269-297. Preprint in Arxiv 2310.19425
• 100 p Q2 • Sun, H., Jin, J., Daly, I., Huang, Y., Zhao, X., Wang, X., & Cichocki, A. (2023). Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. Journal of Neuroscience Methods, 399, 109969. https://doi.org/10.1016/j.jneumeth.2023.109969
• 100 p Q3 • M. Lewandowska, K. Tołpa, M. Hajnowski, T. Piotrowski, J. Dreszer, The less complex temporal patterns of resting‑state EEG activity, the lower the visual temporal order threshold, Acta Neurobiologiae Experimentalis, 83, 2023.
• 70 p • Alexandre L.A, Duch W. (2024) Schizophrenia Detection using EEG: A Study on Frequency Relevance. 2024 IEEE Congress on Evolutionary Computation (CEC), 01–08. https://doi.org/10.1109/CEC60901.2024.10612161
• 70 p Q2 • Bai, G., Jin, J., Xu, R., Wang, X., & Cichocki, A. (2024). A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding. Cognitive Neurodynamics. https://doi.org/10.1007/s11571-023-10053-1 
• 70 p Q1 • Jin, J., He, X., Allison, B. Z., Qin, K., Wang, X., & Cichocki, A. (2024). Leveraging Spatio Temporal Estimation for Online Adaptive Steady State Visual Evoked Potential Recognition. IEEE Transactions on Cognitive and Developmental Systems, 1–13. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2024.3392745
• 70 p Q1 M. Lewandowska, K. Tołpa, J. Rogala, T. Piotrowski, J. Dreszer, Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences, Behavioral and Brain Functions, 19
• 70 p Q2 • Liang, W., Jin, J., Daly, I., Sun, H., Wang, X., & Cichocki, A. (2023). Novel channel selection model based on graph convolutional network for motor imagery. Cognitive Neurodynamics, 17(5), 1283–1296. https://doi.org/10.1007/s11571-022-09892-1
• 70 p Q2 • Liang, W., Jin, J., Xu, R., Wang, X., & Cichocki, A. (2023). Variance characteristic preserving common spatial pattern for motor imagery BCI. Frontiers in Human Neuroscience, 17. https://doi.org/10.3389/fnhum.2023.1243750
• 70 p Q1 • Poetto S, Duch W. (2024) Classification of Schizophrenia EEG Recording Using Homological Features. WCCI IJCNN 2024, IEEE World Congress on Computational Intelligence, Yokohama, Japan, 30.06- 5.07.2024. https://doi.org/10.1109/IJCNN60899.2024.10650296
• 70 p • Szwarc Jakub, Anna Rasmus, Joanna Koziara, Jacek Matulewski. What is supposed to help, actually hinders: about the impact of the GUI dynamics on the efficiency of gaze text entry. Procedia Computer Science 225C, 2291–2300 (2023). doi.org/10.1016/j.procs.2023.10.220
• 70 p • Grech Julia, Jacek Matulewski. Evaluation of several gaze control methods for a board game with no time pressure. Procedia Computer Science 225C, 2456–2465 (2023). doi.org/10.1016/j.procs.2023.10.237
• 70 p • Słupczewski Jakub, Małgorzata Gut, Jacek Matulewski, Adam Tarnowski Numerosity comparison, cognitive strategies and general cognitive functioning in older people. Frontiers in Psychology, 15, 1340146 (2024). doi.org/10.3389/fpsyg.2024.1340146
• 50 p • Finc, K. (11/2023). Network Neuroscience Methods for Investigating Brain Plasticity. In: The Oxford Handbook of Cognitive Enhancement and Brain Plasticity. (online edn, Oxford Academic, 20 Nov. 2023) Link: https://academic.oup.com/edited-volume/55210/chapter-abstract/426833074?redirectedFrom=fulltext
• 20p • Lucas, M., Townsend-Teague, A., Neri, M., Poetto, S., Morris, A., Habermann, B., & Tichit, L. (2023). Phasik: a Python package to identify system states in partially temporal networks. Journal of Open Source Software, 8(91), 5872. DOI: 10.21105/joss.05872
  
• Leitão, A., Lucas, M., Poetto, S., Hersh, T. A., Gero, S., Gruber, D., Bronstein, M., & Petri, G. (2023). Social learning across symbolic cultural barriers in non-human cultures arXiv:2307.05304). https://doi.org/10.48550/arXiv.2307.05304
• Tołpa K, Duch W. (2024) Similarity to Prototypes of Power Maps for Classification of EEG Signals.
  PP-RAI 2024, 5th Polish Conference on Artificial Intelligence. Warsaw, Poland.
• Poetto S, Duch W. (2024)Classification of Schizophrenia EEG recording using homological features
  PP-RAI 2024, 5th Polish Conference on Artificial Intelligence. Warsaw, Poland.
• Alexandre L.A, Duch W. (2024) Electrode Importance for EEG-based Schizophrenia Detection.
  PP-RAI 2024, 5th Polish Conference on Artificial Intelligence. Warsaw, Poland.
• Alexandre L.A, Duch W. (2024) Schizophrenia Detection using EEG: A Study on Frequency Relevance.
  WCCI CEC 2024, IEEE World Congress on Computational Intelligence (IEEE WCCI CEC 2024), Yokohama, Yokohama, Japan, 30.06- 5.07.2024.
• Poetto S, Duch W. (2024) Classification of Schizophrenia EEG Recording Using Homological Features.
  WCCI IJCNN 2024, IEEE World Congress on Computational Intelligence (IEEE WCCI IJCNN 2024), Yokohama, Yokohama, Japan, 30.06- 5.07.2024.

2023

• 140 p • Duch W, Tołpa K, Ratajczak E, Hajnowski M, Furman Ł, Alexandre L. (2023) Asymptotic spatiotemporal averaging of the power of EEG signals for schizophrenia diagnostics. International Conference on Neural Information Processing, Changsha, China. Springer International Publishing, 2023
• 140 p Grąbczewski K., Adamczak R., Grochowski M., Joachimiak M., Sokolov O., Kołodziejczak M., Kowalski P., i Sierakowska K., Mechanical ventilation settings advisory system Odyn, „29th Americas Conference on Information Systems, AMCIS 2023, Panama City, Panama, 10-12.08.2023”, Association for Information Systems 2023, s. 1–10.
• 140 p, Q1 Komorowski M.K, K. Rykaczewski, T. Piotrowski, K. Jurewicz, J. Wojciechowski, A. Keitel, J. Dreszer, W. Duch (2023), ToFFi – Toolbox for Frequency-based Fingerprinting of Brain Signals. Neurocomputing, Volume 544, 1 August 2023, 126236. IF 5,79.
  https://doi.org/10.1016/j.neucom.2023.126236
• 140 p Q1 • Matulewski, J, Bibianna Bałaj, Iga Mościchowska, Agnieszka Ignaczewska, Rafal Linowiecki, Joanna Dreszer, Włodzisław Duch (2022). Learnability Evaluation of the Markup Language for Designing Applications Controlled by Gaze. International Journal of Human-Computer Studies, 165 (2022), 102863.
  https://doi.org/10.1016/j.ijhcs.2022.102863
• 140 p Q1 • Wu, R., Jin, J., Daly, I., Wang, X., & Cichocki, A. (2023). Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 3075–3085. https://doi.org/10.1109/TNSRE.2023.3294815
• 140 p Q1 • Yu, Y., Bezerianos, A., Cichocki, A., & Li, J. (2023). Latent Space Coding Capsule Network for Mental Workload Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 31, 3417–3427. https://doi.org/10.1109/TNSRE.2023.3307481
• 140 p Q2 • Małgorzata Gut, Katarzyna Mańkowska, Jakub Słupczewski, Matulewski, J (2022). Heterogeneity of Dyscalculia Risk Dependent on the Type of Number Line Estimation Task and the Number Magnitude. International Journal of Environmental Research and Public Health, 19(10), 6164. doi.org/10.3390/ijerph19106164
• 100 p Q1 • Kun Xia, Wlodzislaw Duch, Yu Sun, Kedi Xu, Weili Fang, Hanbin Luo, Yi Zhang, Dong Sang, Xiaodong Xu Fei-Yue Wang and Dongrui Wu (2023). Privacy-Preserving Brain-Computer Interfaces: A Systematic Review. IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2312-2324, Oct. 2023. https://doi.org/10.1109/TCSS.2022.3184818 (Cite Score 8.6)
• 100 p • Duch W. (2023), Artificial intelligence and the limits of the humanities. Er(r)go 47 (2/2023) – Humanities, pp. 1-39. Preprint: http://arxiv.org/abs/2310.19425
• 100 p Q2 • Sun, H., Jin, J., Daly, I., Huang, Y., Zhao, X., Wang, X., & Cichocki, A. (2023). Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. Journal of Neuroscience Methods, 399, 109969. https://doi.org/10.1016/j.jneumeth.2023.109969
• 70 p • Q1 M Lewandowska, K Tołpa, J Rogala, T Piotrowski, J Dreszer, Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences. Behavioral and Brain Functions 19, 18 (2023). https://doi.org/10.1186/s12993-023-00218-7
• 70 p • Matulewski, J, Mateusz Patera Usability of the super-vowel for gaze-based text entry. ETRA ’22: 2022 Symposium on Eye Tracking Research and Applications. Association for Computing Machinery, New York, NY, USA, Article 69, 1–5 (2022). doi.org/10.1145/3517031.3529231
• Dereziński K, Tołpa K, Furman Ł, Rutkowski T.M, Duch W. (2023). Time-frequency analysis combined with recurrence quantification for classification of onset of dementia using data from the oddball BCI paradigm,” 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), Ise, Japan, 2022, pp. 1-6, doi: 10.1109/SCISISIS55246.2022.10001959
• 100p, Q1, T Piotrowski, M Frey, RLG Cavalcante, R Ismailov (2022). Inverse Solvability and Security with Applications to Federated Learning, IEEE Signal Processing Letters, submitted. Preprint https://arxiv.org/abs/2211.14115
• Czekaj Ł, Radzinski Ł, Kolimaga M, Domaszewicz J, Kitłowski R, Szwoch M, Duch W. (2023) On the recognition of the type of game based on physiological signals and eye tracking. In: Modelling and Simulation 2023. The European Simulation and Modelling Conference 2023 / Rob Vingerhoeds, Pierre de Saqui-Sannes, Geril Philippe (eds.), 2023, EUROSIS-ETI, pp.235-238 arXiv.2310.17383
• Leitão, A., Lucas, M., Poetto, S., Hersh, T. A., Gero, S., Gruber, D., Bronstein, M., & Petri, G. (2023). Social learning across symbolic cultural barriers in non-human cultures (arXiv:2307.05304). https://doi.org/10.48550/arXiv.2307.05304
• Furman Ł, Tołpa K, Alexandre L, Duch W. (2023). Recurrence analysis of brain neurodynamics. Nonlinear Data Analysis and Modeling: Advances, Applications, Perspectives 15–17.03.2023, Potsdam, Germany.
• Komorowski, M.K, Rykaczewski, K, Piotrowski, T, Jurewicz, K, Wojciechowski, J, Keitel, A, Dreszer, J, Duch, W. (2023) Toolbox for Frequency-based Fingerprinting. TOFFI Toolbox.
  4-th PP-RAI Conference, Technical University of Łódź, 2023.04.24-26.
• Duch W, Tołpa K, Furman Ł, Ratajczak E, Hajnowski M, Alexandre L. (2023) Analysis of brain activity based on long-term averaging of EEG signals, and poster
  4th Polish Conference on Artificial Intelligence (PP-RAI 4), Łódź, 26-29.04.2023
• 2022
• Sokolov, O., Mrela, A., Bieniek-Majka, M., Osinska, V., & Duch, W. (2022). Model of Trust Dissemination of Products Based on Fuzzy Aggregation Norms. 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), 741–744. https://doi.org/10.15439/2022F182
• Paweł Drojecki, Wojciech Sobczuk, Jacek Matulewski Gaze Buttons Revisited.Looking for an Effective Way of “Clicking” without the Mouse. Procedia Computer Science 207, 1539–1551 (2022). doi.org/10.1016/j.procs.2022.09.211
• Jacek Matulewski, Mateusz Patera Usability of the super-vowel for gaze-based text entry. ETRA ’22: 2022 Symposium on Eye Tracking Research and Applications. Association for Computing Machinery, New York, NY, USA, Article 69, 1–5 (2022). doi.org/10.1145/3517031.3529231
• Daly, W Liang, J Jin, I Daly, H Sun, X Wang, A Cichocki “Novel channel selection model based on graph convolutional network for motor imagery”. Cognitive Neurodynamics (October 2022). https://doi.org/10.1007/s11571-022-09892-1
• Duch W. (2022) Concept Representation and the Geometric Model of Mind. Studies in Logic, Grammar and Rhetoric, 67 (60), 151 – 167. doi: 10.2478/slgr-2022-0009
• Gong, S., Xing, K., Cichocki, A. and Li, J., (September 2022). “Deep Learning in EEG: Advance of the Last Ten-Year Critical Period”. IEEE Transactions on Cognitive and Developmental Systems. 14 (2), 348-365. DOI: 10.1109/TCDS.2021.3079712
• X Zhao, J Jin, R Xu, S Li, H Sun, X Wang, A Cichocki, “A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller. Frontiers in Human Neuroscience, 2022, https://www.frontiersin.org/articles/10.3389/fnhum.2022.875851/pdf
• Liu C, Jin J, Daly I, Sun H, Huang Y, Wang X, Cichocki A. “Bispectrum-based hybrid neural network for motor imagery classification”. Journal of Neuroscience Methods. 2022 June 1; https://doi.org/10.1016/j.jneumeth.2022.109593
• Huang Y, Jin J, Xu R, Miao Y, Liu C, Cichocki A. “Multi-view optimization of time-frequency common spatial patterns for brain-computer interfaces”. Journal of Neuroscience Methods. 2022 Jan 1;365:109378.
• Małgorzata Gut, Katarzyna Mańkowska, Jakub Słupczewski, Jacek Matulewski (2022). Heterogeneity of Dyscalculia Risk Dependent on the Type of Number Line Estimation Task and the Number Magnitude. International Journal of Environmental Research and Public Health, 19(10), 6164. doi.org/10.3390/ijerph19106164
• Guiomar Niso, Rotem Botvinik-Nezer, Stefan Appelhoff, Alejandro De La Vega, Oscar Esteban, Joset A Etzel, Karolina Finc, Melanie Ganz, Rémi Gau, Yaroslav O Halchenko, Peer Herholz, Agah Karakuzu, David B Keator, Christopher J Markiewicz, Camille Maumet, Cyril R Pernet, Franco Pestilli, Nazek Queder, Tina Schmitt, Weronika Sójka, Adina S Wagner, Kirstie J Whitaker, Jochem W Rieger (2022). Open and reproducible neuroimaging: from study inception to publication. NeuroImage, 119623. https://doi.org/10.1016/j.neuroimage.2022.119623
• T. Wang, A. Bezerianos, A. Cichocki and J. Li, “Multikernel Capsule Network for Schizophrenia Identification,” in IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 4741-4750, June 2022, doi: 10.1109/TCYB.2020.3035282.
• J. Jin, H. Sun, I. Daly,S. Li,C. Liu, X. Wang, A. Cichocki, “A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 20-29, 2022, doi: 10.1109/TNSRE.2021.3139095.
• C. Liu, J. Jin, I. Daly, S. Li, H. Sun, Y. Huang, X. Wang, A. Cichocki “SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 540-549, 2022, doi: 10.1109/TNSRE.2022.3156076.
• Li S, Jin J, Daly I, Liu C, Cichocki A. “Feature selection method based on Menger curvature and LDA theory for a P300 brain–computer interface”. Journal of Neural Engineering. 2022 4;18(6):066050. https://doi.org/10.1088/1741-2552/ac42b4
• Qu, T., Jin, J., Xu, R., Wang, X., & Cichocki, A. (September 2022). “Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs”, Vol 19, No 5, Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ac9338
• J. Jin, T. Qu, R. Xu, X. Wang and A. Cichocki, “Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, October 2022, 140 p, doi: 10.1109/TNSRE.2022.3217573.
• Visser, C Bergmann, K Byers-Heinlein, R Dal Ben, W Duch, S Forbes, L. Franchin, M. Frank, A. Geraci, J.K.Hamlin, Z. Kaldy, L. Kulke, C. Laverty, C. Lew-Williams, V. Mateu, J. Mayor, D. Moreau, I. Nomikou, T. Schuwerk, E. Simpson, L. Singh, M. Soderstrom, J. Sullivan, M. Van den Heuvel, G. Westermann, Y. Yamada, L. Zaadnoordijk, M. Zettersten, Improving the generalizability of infant psychological research: The Many Babies model. Behavioral and Brain Sciences (Feb 2022, IF 12.579). DOI: https://doi.org/10.1017/S0140525X21000455
• Zhang, Y., Zhou, T., Wu, W., Xie, H., Zhu, H., Zhou, G., & Cichocki, A. “Improving EEG Decoding via Clustering-Based Multitask Feature Learning,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, pp. 3587-3597, Aug. 2022, doi: 10.1109/TNNLS.2021.3053576.
• Lewandowska, Monika; Nikadon, Jan; Wolak, Tomasz; Tołpa, Krzysztof; Piotrowski, Tomasz; Chojnowski, Mateusz; Dreszer, Joanna; Resting-state fMRI functional connectivity of the left temporal parietal junction is associated with visual temporal order threshold, Scientific reports, 12, 1, 1-12, 2022.
• Piotrowski, Tomasz; Cavalcante, Renato LG; The fixed point iteration of positive concave mappings converges geometrically if a fixed point exists: implications to wireless systems,IEEE Transactions on Signal Processing, 70, 4697-4710, 2022.
• Kono, Takehiro; Yukawa, Masahiro; Piotrowski, Tomasz; Relaxed zero-forcing beamformer under temporally-correlated interference,Signal Processing, 190, 108323, 2022.
• Book: Kopcewicz J, Duch W, Strobel A, Kosmos i życie. Wyd. Naukowe UMK (2/2022).
• Duch W. (2022). Imagery agnosia and its phenomenology. Annals of Psychology, pp. 1-17 DOI https://doi.org/10.18290/rpsych21242-5s.
• Matulewski J, Bałaj B, Mościchowska I, Ignaczewska A, Linowiecki R, Dreszer J, Duch W. (2022) Learnability Evaluation of the Gaze Interaction Markup Language for a simple design of new applications. International Journal of Human-Computer Studies 165, 1-14, art # 102863.
• Kun Xia, Wlodzislaw Duch, Yu Sun, Kedi Xu, Weili Fang, Hanbin Luo, Yi Zhang, Dong Sang, Xiaodong Xu Fei-Yue Wang and Dongrui Wu (2022). Privacy-Preserving Brain-Computer Interfaces: A Systematic Review. IEEE Transactions on Computational Social Systems, https://doi.org/10.1109/TCSS.2022.3184818
• Kun Xia, Lingfei Deng, Wlodzislaw Duch, , Dongrui Wu. (2022) Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces. IEEE Transactions on Biomedical Engineering, https://doi.org/10.1109/TBME.2022.3168570 (IF 4.76)
• Furman Ł, Tołpa K, Minati L, Duch W. (2022) Short-Time Fourier Transform and Embedding Method for Recurrence Quantification Analysis of EEG Time Series. The European Physical Journal Special Topics (sub. 4/22, rev. 7/22)
• 2021
• Rykaczewski, Krzysztof; Nikadon, Jan; Duch, Włodzisław; Piotrowski, Tomasz; supFunSim: spatial filtering toolbox for EEG, Neuroinformatics, 19, 1, 107-125,2021.
• Gut, M, Finc K, Matulewski J, Goraczewski Ł, Walerzak-Więckowska A, Duch W. (2021) Number line estimation strategies used by children with dyscalculia and healthy controls. The Review of Psychology 64(3). DOI: https://doi.org/10.31648/pp.7353
• Gut, M., Binder, M., Finc, K., & Szeszkowski, W. (2021). Brain activity underlying response induced by SNARC‑congruent and SNARC‑incongruent stimuli. Acta Neurobiol Exp, 81(2):121-140.
• Levitis, E., van Praag, C. D. G., Gau, R., Heunis, S., DuPre, E., Kiar, G., … Finc K, … & Maumet, C. (2021). Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective. GigaScience, 10(8), giab051.
• I Visser, C Bergmann, K Byers-Heinlein, R Dal Ben, W Duch, S Forbes, L. Franchin, M. Frank, A. Geraci, J.K.Hamlin, Z. Kaldy, L. Kulke, C. Laverty, C. Lew-Williams, V. Mateu, J. Mayor, D. Moreau, I. Nomikou, T. Schuwerk, E. Simpson, L. Singh, M. Soderstrom, J. Sullivan, M. Van den Heuvel, G. Westermann, Y. Yamada, L. Zaadnoordijk, M. Zettersten (2021)
  Improving the generalizability of infant psychological research: The ManyBabies model. Behavioral and Brain Sciences
• Duch W. (2021). Memetics and Neural Models of Conspiracy Theories. Patterns. Cell Press.
• Ratajczak, E., Hajnowski, M., Stawicki, M., & Duch, W. (2021). Novel Methodological Tools for Behavioral Interventions: The Case of HRV-Biofeedback. Sham Control and Quantitative Physiology-Based Assessment of Training Quality and Fidelity. Sensors (MDPI), 21(11), 3670.
• Piotrowski, T., Nikadon, J., & Moiseev, A. (2021). Localization of brain activity from EEG/MEG using MV-PURE framework. Biomedical Signal Processing and Control, 64, 102243. https://doi.org/10.1016/j.bspc.2020.102243
• Rykaczewski, K., Nikadon, J., Duch, W. et al. SUPFUNSIM: Spatial Filtering Toolbox for EEG. Neuroinform 19, 107–125 (2021). https://doi.org/10.1007/s12021-020-09464-w
• Li, S., Jin, J., Daly, I., Wang, X., Lam, H. K., & Cichocki, A. (2021). Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method. Journal of Neuroscience Methods, 362, 109300. 100 p, IF 2,390, Q3
• Z. Wang, J. Jin, R. Xu, C. Liu, X. Wang and A. Cichocki, “Efficient Spatial Filters Enhance SSVEP Target Recognition Based on Task-Related Component Analysis,” in IEEE Transactions on Cognitive and Developmental Systems, doi: 10.1109/TCDS.2021.3096812. 70p, IF 3,379, Q2
• Liu, C., Jin, J., Xu, R., Li, S., Zuo, C., Sun, H., & Cichocki, A. (2021). Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain–computer interface. Journal of Neural Engineering, 18(4), 0460e4. 140p, IF 5,379, Q1
• Duan, F., Jia, H., Zhang, Z., Feng, F., Tan, Y., Dai, Y., Cichocki A. & Solé-Casals, J. (2021). On the robustness of EEG tensor completion methods. Science China Technological Sciences, 1-15. 70p,
• Jin, J., Fang, H., Daly, I., Xiao, R., Miao, Y., Wang, X., & Cichocki, A. (2021). Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI. International Journal of Neural Systems, 31(07), 2150030. 140p, IF 5,866, Q1
• Miao, Y., Jin, J., Daly, I., Zuo, C., Wang, X., Cichocki, A., & Jung, T. P. (2021). Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 699-707. 140p, IF 3,802, Q1
• Zhang, X., Jin, J., Li, S., Wang, X., & Cichocki, A. (2021). Evaluation of color modulation in visual P300-speller using new stimulus patterns. Cognitive Neurodynamics, 1-14. 70p, IF 5,082, Q2
• Mao, Y., Jin, J., Xu, R., Li, S., Miao, Y., & Cichocki, A. (2021). The influence of visual attention on the performance of a novel tactile P300 brain-computer interface with cheeks-stim paradigm. International Journal of Neural Systems, 31(04), 2150004. 140p, IF 5,866, Q1
• Sun, H., Jin, J., Xu, R., & Cichocki, A. (2021). Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain–Computer Interfaces. International Journal of Neural Systems, 31(09), 2150040. 140p, IF 5,866, Q1
• Komorowski, J. Dreszer, K. Jurewicz, J. Wojciechowski, T. Piotrowski, W. Duch. (2021) Spectral Features of Human Brain Activity Are Related to Sensory-Associative Cortical Gradient. 27th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2021), 30.05-04.06.2021
• 2020
• Bonna, K, Finc, K, Zimmermann, M., Bola, Ł., Mostowski, P., Szul, M., Rutkowski, P., Duch, W., Marchewka, A., Jednoróg, K., Szwed, M. (2020).
  Early deafness leads to re-shaping of global functional connectivity beyond the auditory cortex. Brain Imaging and Behaviour, 1-14.
• Finc, K., Bonna, K., He, X., Lydon-Staley, D. M., Kühn, S., Duch, W., & Bassett, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training. Nature Communications 11, 2435.
• Bielczyk, N. Z., Ando*, A., Badhwar*, A., Caldinelli*, C., Gao*, M., Haugg*, A., Hernandez*, L. M., Ito*, K., Kessler*, D., Lurie*, D., Makary*, M. M., Nikolaidis*, A., Veldsman*, M., Allen, C., Bankston, A., Costa Boffino, C., Bottenhorn, K. L., Braukmann, R., Cheplygina, V., Ercan, E., Finc, K., …, OHBM Student and Postdoc Special Interest Group. Effective self-management for early career researchers in the natural and life sciences. Neuron 106(2), 212–217
• Dreszer J, Marek Grochowski, Monika Lewandowska, Jan Nikadon, Joanna Gorgol, Bibianna Bałaj, Karolina Finc, Włodzisław Duch, Tomasz Piotrowski. (2020) Spatiotemporal Complexity Patterns of the Frontoparietal Network Predict Fluid Intelligence: Sex Matters. Human Brain Mapping 41(17), 4846-4865
• Asanowicz, D., Gociewicz, K., Koculak, M., Finc, K., Bonna, K., Cleeremans, A., & Binder, M. (2020).
  The response relevance of visual stimuli modulates the P3 component and the underlying sensorimotor network. Scientific Reports, 10(1), 1-20.
• Thompson, W. H., Kastrati, G., Finc, K., Wright, J., Shine, J. M., & Poldrack, R. A. (2020). Time‐varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation. Human Brain Mapping, 41(9), 2347-2356.
• Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., … & Gorgolewski, K. J. (2020). Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nature Protocols.
• A. Phan, A. Cichocki, I. Oseledets, G. G. Calvi, S. Ahmadi-Asl and D. P. Mandic, “Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition,” in IEEE Transactions on Neural Networks and Learning Systems (IF=11.6), vol. 31, no. 6, pp. 2174-2188, June 2020, doi: 10.1109/TNNLS.2019.292906
• Phan, A. H., Cichocki, A., Uschmajew, A., Tichavský, P., Luta, G., & Mandic, D. P. (2020). “Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation”. IEEE Transactions on Neural Networks and Learning Systems IF=11.6 (accepted) doi: 10.1109/TNNLS.2019.2956926
• Duch W, Mikołajewski D. (2020) Modelling effects of consciousness disorders in brainstem computational model – Preliminary findings.
  Bio-Algorithms and Med-Systems 16(2), 1-10, 20190059, 2020.
• Duch W. (2020) Meta-analizy w badaniach nad nabywaniem języka. Psychologia Rozwojowa 25(1), 9-27.
• Meina M, Ratajczak E, Sadowska M, Rykaczewski K, Dreszer J, Bałaj B, Biedugnis S, Węgrzyński W, Krasuski A. Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters. Sensors. 2020; 20(10):2834. https://doi.org/10.3390/s20102834
• Lehky, S. R., Phan, A. H., Cichocki, A., & Tanaka, K. (2020). “Face Representations via Tensorfaces of Various Complexities”. Neural Computation, 32(2), 281-329.
• F. Sedighin, A. Cichocki, T. Yokota and Q. Shi, “Matrix and Tensor Completion in Multiway Delay Embedded Space Using Tensor Train, with Application to Signal Reconstruction,” in IEEE Signal Processing Letters, vol. 27, pp. 810-814, 2020, doi: 10.1109/LSP.2020.2990313.
• Pan, J., Xie, Q., Qin, P., Chen, Y., He, Y., Huang, H., Wang, F., Ni, X., Cichocki, A., Yu, R. and Li, Y., 2020. Prognosis for Patients with Cognitive Motor Dissociation Identified by Brain-Computer Interface. Brain, 143(4), pp.1177-1189.
• Chen, Z., Jin, J., Daly, I., Zuo, C., Wang, X., & Cichocki, A. (2020). Effects of Visual Attention on Tactile P300 BCI. Computational Intelligence and Neuroscience, 2020.
• Liu, Q., Jiao, Y., Miao, Y., Zuo, C., Wang, X., Cichocki, A., & Jin, J. (2020). Efficient Representations of EEG Signals for SSVEP Frequency Recognition Based on Deep Multiset CCA. Neurocomputing, 378, 36-44.
• Akter, M.S., Islam, M.R., Iimura, Y., Sugano, H., Fukumori, K., Wang, D., Tanaka, T. and Cichocki, A., 2020. Multiband Entropy-based Feature-extraction Method for Automatic Identification of Epileptic Focus Based on High-frequency Components in Interictal iEEG. Scientific Reports, 10(1), pp.1-17.
• Jin, J., Xiao, R., Daly, I., Miao, Y., Wang, X., & Cichocki, A. (2020). Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory. IEEE Transactions on Neural Networks and Learning Systems. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9174829
• Jin, J., Liu, C., Daly, I., Miao, Y., Li, S., Wang, X., & Cichocki, A. (2020). Bispectrum–based Channel Selection for Motor Imagery based Brain-Computer Interfacing. IEEE Transactions on Neural Systems and Rehabilitation Engineering (accepted in print); https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9184092
• Li, S., Jin, J., Daly, I., Zuo, C., Wang, X., & Cichocki, A. (2020). Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns. Frontiers in Neuroscience, 14, 54. https://www.frontiersin.org/articles/10.3389/fnins.2020.00054/full
• Duan, F., Huang, Z., Sun, Z., Zhang, Y., Zhao, Q., Cichocki, A., Yang, Z. and Solé-Casals, J., 2020. Topological Network Analysis of Early Alzheimer’s Disease Based on Resting-State EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
• Zuo C, Jin J, Yin E, Saab R, Miao Y, Wang X, Hu D, Cichocki A. Novel hybrid brain–computer interface system based on motor imagery and P300. Cognitive Neurodynamics. 2020 Apr;14(2):253-65.
• Peng, Y., Li, Q., Kong, W., Qin, F., Zhang, J. and Cichocki, A., 2020. A joint optimization framework to semi-supervised RVFL and ELM networks for efficient data classification. Applied Soft Computing, p.106756.
• Ahmadi-Asl, S., Cichocki, A., Phan, A. H., Asante-Mensah, M. G., Mousavi, F., Oseledets, I., & Tanaka, T. (2020). Randomized algorithms for fast computation of low-rank tensor ring model. Machine Learning: Science and Technology.
• T. Wang, A. Bezerianos, A. Cichocki J. Li Multi-Kernel Capsule Network for Schizophrenia Identification. IEEE Transactions on Cybernetics (2020).
• Jin, J., Li, S., Daly, I., Miao, Y., Liu, C., Wang, X., & Cichocki, A. (2020). The study of generic model set for reducing calibration time in P300-based Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, VOL. 28, NO. 1, January 2020.
• Yokota, T., Hontani, H., Zhao, Q. and Cichocki, A., 2020. Manifold Modeling in Embedded Space: A Perspective for Interpreting” Deep Image Prior”. IEEE Transactions Neural Networks and Learning Systems
• J. Matulewski, M.Patera, Comparison of three dwell-time-based gaze text entry methods: Extended Abstract. ACM Symposium on Eye Tracking Research and Applications (ETRA ’20 Adjunct). Association for Computing Machinery, New York, NY, USA, Article 15, 1–5 (2020). doi:10.1145/3379157.3388931
• Ratajczak E., Hajnowski M., Czachowski S. (2020), HRV Biofeedback – terapia przez samokontrolę rytmu serca. Nowe Technologie i Metody w Psychologii, red. Gut, Trojan
• Lewandowska M., Milner R., Pawlaczyk N., Ratajczak E. (2020) Zastosowanie analizy ilościowej sygnału EEG (qEEG) we współczesnej psychologii i psychiatrii. Nowe Technologie i Metody w Psychologii, red. Gut, Trojan
• Czachowski S., Dereziński K., Lewandowska M., Milner R., Ratajczak E., Stawicki M., Duch W. (2020), EEG-Neurofeedback w terapii bólu przewlekłego: aktualny stan wiedzy i dalsze kierunki badań. Nowe Technologie i Metody w Psychologii, red. Gut, Trojan
• Duch W, Mikołajewski D. (2020) Modelling effects of consciousness disorders in brainstem computational model – Preliminary findings. Bio-Algorithms and Med-Systems 16(2), 1-10, 20190059, 2020.
• Duch W. (2020) Experiential Learning Styles and Neurocognitive Phenomics. PsyArXiv. August 30. doi:10.31234/osf.io/s3te7
• 2019
• Aghabeig, Mansoureh; Bałaj, Bibianna; Dreszer, Joanna; Lewandowska, Monika; Milner, Rafał; Pawlaczyk, Natalia; Piotrowski, Tomasz; Szmytke, Magdalena; Duch, Włodzisław; Perception of non-native phoneme contrasts in 8-13 months infants: tensor-based analysis of EEG signals, 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019.
• Kono, Takehiro; Yukawa, Masahiro; Piotrowski, Tomasz; Beamformer design under time-correlated interference and online implementation: brain-activity reconstruction from EEG, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1070-1074, 2019.
• Piotrowski, Tomasz; Nikadon, Jan; Gutiérrez, David, MV-PURE spatial filters with application to EEG/MEG source reconstruction,IEEE Transactions on Signal Processing, 67, 3, 553-567, 2019.
• Zuo, Cili, Jing Jin, Erwei Yin, Rami Saab, Yangyang Miao, Xingyu Wang, Dewen Hu, and Andrzej Cichocki. “Novel hybrid brain–computer interface system based on motor imagery and P300.” Cognitive Neurodynamics (2019): 1-13.
• Feng, J. K., Jin, J., Daly, I., Zhou, J., Niu, Y., Wang, X., & Cichocki, A. (2019). “An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system”. Computational Intelligence and Neuroscience, 2019.https://www.hindawi.com/journals/cin/2019/8068357/abs/
• Cheng, J., Jin, J., Daly, I., Zhang, Y., Wang, B., Wang, X., & Cichocki, A. (2019).  “Effect of a combination of flip and zooming stimuli on the performance of a visual brain computer interface for spelling” Biomedical Engineering/Biomedizinische Technik, 64(1), 29-38. https://www.degruyter.com/view/j/bmte.2019.64.issue-1/bmt-2017-0082/bmt-2017-0082.xml
• Miao, Yangyang, Erwei Yin, Brendan Z. Allison, Yu Zhang, Yan Chen, Yi Dong, Xingyu Wang, Dewen Hu, Andrzej Cichocki, and Jing Jin, “An ERP-based BCI with peripheral stimuli: Validation with ALS patients.” Cognitive Neurodynamics: 1-13.
• Jin, J., Miao, Y., Daly, I., Zuo, C., Hu, D., & Cichocki, A. (2019), “Correlation-based channel selection and regularized feature optimization for MI-based BCI”. Neural Networks, 118, 262-270.
• Guo, M., Jin, J., Jiao, Y., Wang, X., & Cichocki, A. (2019). Investigation of visual stimulus with various colors and the layout for the oddball paradigm in ERP-based BCI. Frontiers in Computational Neuroscience, 13, 24.
• Zhang, Z., Duan, F., Solé-Casals, J., Dinarès-Ferran, J., Cichocki, A., Yang, Z., & Sun, Z. (2019). A novel deep learning approach with data augmentation to classify motor imagery signals. IEEE Access, 7, 15945-15954.

• Accepted
• 100 p Q1 • Cichocki, A., Cruces, S., Sarmiento, A., & Tanaka, T. (2024). „Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection”. arXiv preprint arXiv:2406.00655. (accepted in IEEE Access).100 p Q1 • Duch W, Tołpa K, Furman Ł, Ratajczak E. (2024) EEG Biomarkers based on Microstates and RQA. Brain Informatics (Springer), The 17th International Conference on Brain Informatics (BI 2024) – Brain Science meets Artificial Intelligence.200 p Q1 • Piotrowski T, RLG Cavalcante, M Gabor, Fixed points of nonnegative neural networks,
  NeurNIPS, 12/2024.
• Submitted, in reviews:
• 200 p • Q1 Ewa Ratajczak, Marcin Hajnowski, Krzysztof Tołpa, Martyna Olszewska, Włodzisław Duch. Predicting creativity improvements following HRV biofeedback training with EEG microstate neurodynamics, Artificial Intelligence for Education: A Signal Processing Perspective (S1). IEEE Signal Processing Magazine.
• 20 p • Q1 Bonna, K., Hulme, O. J., Meder, D., Duch, W., & Finc, K. (2023). Brain network reconfiguration during prediction error processing. Submited to Network Neuroscience (MIT Press), https://doi.org/10.1101/2023.07.14.549018
• 40 p • Izabela Grzankowska, Jacek Matulewski, Barbara Golasik, Wojciech Sobczuk, Katarzyna Matulewska Kształcenie umiejętności życia codziennego w grupie młodzieży ze spektrum autyzmu z użyciem gry terapeutycznej – wstępne wyniki, Niepełnosprawność. Dyskursy pedagogiki specjalnej
• Bałaj B, Matulewski J, Szmytke M, Pawlaczyk N, Dreszer J, Duch W.
  The use of gaze interaction to study speech sound discrimination training in infants.
• Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Marcus Frohme and Alina Nechyporenko, Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models. Journal: Frontiers in Microbiology, section Systems Microbiology
  Article type: Review. Manuscript ID: 1516667, Submitted on: 24.10.2024
• Bonna, K., Hulme, O. J., Meder, D., Duch, W., & Finc, K. (2023). Brain network reconfiguration during prediction error processing (p. 2023.07.14.549018). bioRxiv, submitted to Network Neuroscience (MIT Press), https://doi.org/10.1101/2023.07.14.549018
• Martyna Olszewska, Daria Makurat, Marcin Hajnowski, Ewa Ratajczak. (2023) Creative products of artistically creative individuals are judged as more aesthetic. Psychology of Aesthetics, Creativity, and the Arts (in review)
• Alexandre, L, Duch W. (2024) Schizophrenia Detection using EEG: A Study on Frequency Relevance.
• Bałaj B, Matulewski J, Szmytke M, Pawlaczyk N, Dreszer J, Duch W. (2023) The use of gaze interaction to study speech sound discrimination training in infants.

Conference papers, preprints, and other publications:

20 p • Alexandre L.A, Duch W. (2024) Electrode Importance for EEG-based Schizophrenia Detection. PP-RAI 2024, Proc. 5th Polish Conference on Artificial Intelligence (PP-RAI 2024), pp. 86-91. DOI:10.17388/WUT.2024.0002.MiNI.

70 p • Katarzyna Kotyńska, Jacek Matulewski Usability study of gaze-based control methods in a game with time pressure. Procedia Computer Science XXX, XXXX–XXXX (2024). doi.org/10.1016/j.procs.2024.XX.XX

70 p • Albert Łukasik, Jacek Matulewski, Klaudia Karkowska, Izabela Grzankowska, Michał Joachimiak, Dawid Pietrykowski, Gabriela Ciesielska, Marcin Sztramski Do we need to squeeze large tele-AR scenes to fit them into a small room?. Procedia Computer Science XXX, XXXX–XXXX (2024). doi.org/10.1016/j.procs.2024.XX.XX

Leitão, A., Lucas, M., Poetto, S., Hersh, T. A., Gero, S., Gruber, D., Bronstein, M., & Petri, G. (2023). Social learning across symbolic cultural barriers in non-human cultures (arXiv:2307.05304). https://doi.org/10.48550/arXiv.2307.05304

Matulewski J, Visual Studio 2022. Wprowadzenie do .NET MAUI, Wydawnictwo Helion, 2023 (ISBN: 978-83-283-9026-3)

Kaniewski Damian, Tomasz Dziubak, Jacek Matulewski MonoGame. Podstawowe koncepcje grafiki 3D Wydawnictwo Helion, 2023 (ISBN: 978-83-283-9350-9) – wyróżnienie w konkursie PTI na książkę informatyczną 2024

Poetto S. (2024) “Topological scaffolds outperform functional connectivity in brain fingerprinting.” NetsciX-24, International School and Conference on Network Science, Venice, Italy.

Poetto S. (2024) “Homological cycles of Functional Connectivity delimit highly synergistic brain areas”. The Fifth International Convention on the Mathematics Of Neuroscience and AI, Rome, Italy.

Poetto S. (2024) “Topological scaffolds outperform functional connectivity in brain fingerprinting”. CCS24 Conference on Complex Systems , Exeter, UK.

Poetto S, Duch W. (2024) Classification of Schizophrenia EEG recording using homological features. PP-RAI 2024, 5th Polish Conference on Artificial Intelligence. Warsaw, Poland.
https://doi.org/10.1093/oxfordhb/9780197677131.013.13

Tołpa K, Duch W. (2024) Similarity to Prototypes of Power Maps for Classification of EEG Signals PP-RAI 2024, 5th Polish Conference on Artificial Intelligence. Warsaw, Poland.

In preparation

• de Domenico M., Grasia, M., Finc, K., Mangioni, G. Machine-enhanced reconstruction of functional connectomes unravels discriminative brain sub-systems in health and disease (to be submitted in PNAS).
• Lewartowski, A., Finc, K., The influence of musical rhythm training on cognitive enhancement and brain plasticity.
• Furman Łukasz, Minati Ludovico, Duch Włodzisław, Highlighting Temporal Patterns in Resting-State EEG Using Autoencoder Latent Spaces and Multivariate Angular Distance Analysis.
• Furman Łukasz, Minati Ludovico, Duch Włodzisław, Cortical-Thalamic Information Transfer in Mild Cognitive Impairment: A Study of Theta-Gamma and Alpha-Beta Coupling Dynamics.
• Furman Łukasz, Minati Ludovico, Duch Włodzisław, Recurrence Quantitative Analysis of EEG Data in Mild Cognitive Impairment: A Focus on Default Mode and Executive Networks.
• Jacek Matulewski, Izabela Grzankowska, Barbara Golasik, Wojciech Sobczuk, Katarzyna Matulewska Developing Personal Competencies of Adolescents with Autism Spectrum Disorder Using the Therapeutic Game Hello, Earth! (do Journal of Autism and Developmental Disorders)
• Jacek Matulewski, Mateusz Patera Gaze Text Entry With The Use Of Super-Vowel (do JoHCS)
• Łukasz Sikorski, Małgorzata Czerwonka, Jacek Matulewski The attitudes towards (generative) AI during video games development among gamedev professionals and students (do Games and Culture)
• Jacek Matulewski, Albert Łukasik, Izabela Grzankowska, Dawid Pietrykowski, Michał Joachimiak, Klaudia Karkowska, Gabriela Ciesielska, Marcin Sztramski Depth perception and associated discomfort in limited room space on low-cost smartphone headsets (do Human Computer Interaction)
• Jacek Matulewski, et al. Badanie gestów prostych (grant Vobacom z NCBiR)
• Jacek Matulewski, et al. Pomiary zmęczenia z użyciem biometrii podczas korzystania z AR (analiza wspólnie z WMiI).
• N. Jankowski, Analysis of new fast clustering algorithms—Revdbscan and flexscan—supported by new methods of searching nearest neighbors. Journal of Artificial Intelligence and Soft Computing Research, 2023/24 (invited, in prep), 140 p
• N. Jankowski & G Dudek, Automatic kernel construction during the neural network learning by modified fast singular value decomposition. Electroinics, Special Issue “Computational Intelligence and Machine Learning: Models and Applications”, 2023/24 (in prep), 140 p
• N. Jankowski, Auto-clustering – a new way of clustering algorithms. International Conference on Machine Learning, 2024, (in prep), 200 p
• N. Jankowski, Comparison and analysis of algorithms for fast computation determining nearest neighbors, Knowledge-Based Systems, 2024, (in prep), 200 p
• N. Jankowski, Fast prototypes selection by Trust-Margin, information sciences, 2024 (in prep), p 200