R Core Team. (2022). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

Google Scholar 

Buzsáki, G. (2005). Theta rhythm of navigation: link between path integration and landmark navigation, episodic and semantic memory. Hippocampus, 15(7), 827–840.

Article  PubMed  Google Scholar 

Buzsáki, G., & Moser, E. I. (2013). Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience, 16(2), 130–138.

Article  PubMed  PubMed Central  Google Scholar 

Chrastil, E. R., Rice, C., Goncalves, M., Moore, K. N., Wynn, S. C., Stern, C. E., & Nyhus, E. (2022a). Theta oscillations support active exploration in human spatial navigation. NeuroImage, 262, 119581.

Chrastil, E. R., Rice, C., Goncalves, M., Moore, K. N., Wynn, S. C., Stern, C. E., & Nyhus, E. (2022b). Theta oscillations support active exploration in human spatial navigation. NeuroImage, 262, 119581.

Commins, S., Duffin, J., Chaves, K., Leahy, D., Corcoran, K., Caffrey, M., Keenan, L., Finan, D., & Thornberry, C. (2020). Navwell: A simplified virtual-reality platform for spatial navigation and memory experiments. Behavior Research Methods, 52, 1189–1207.

Article  PubMed  Google Scholar 

Coughlan, G., Laczó, J., Hort, J., Minihane, A.-M., & Hornberger, M. (2018). Spatial navigation deficits—overlooked cognitive marker for preclinical alzheimer disease? Nature Reviews Neurology, 14(8), 496–506.

Article  PubMed  Google Scholar 

Coughlan, G., Puthusseryppady, V., Lowry, E., Gillings, R., Spiers, H., Minihane, A.-M., & Hornberger, M. (2020). Test-retest reliability of spatial navigation in adults at-risk of alzheimer’s disease. PLoS One, 15(9), e0239077.

Article  PubMed  PubMed Central  Google Scholar 

Crespo-García, M., Zeiller, M., Leupold, C., Kreiselmeyer, G., Rampp, S., Hamer, H. M., & Dalal, S. S. (2016). Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall. Neuroimage, 142, 533–543.

Article  PubMed  Google Scholar 

Du, Y. K., Liang, M., McAvan, A. S., Wilson, R. C., & Ekstrom, A. D. (2023). Frontal-midline theta and posterior alpha oscillations index early processing of spatial representations during active navigation. Cortex, 169, 65–80.

Article  PubMed  Google Scholar 

Greenberg, J. A., Burke, J. F., Haque, R., Kahana, M. J., & Zaghloul, K. A. (2015). Decreases in theta and increases in high frequency activity underlie associative memory encoding. Neuroimage, 114, 257–263.

Article  PubMed  Google Scholar 

Herweg, N. A., Solomon, E. A., & Kahana, M. J. (2020). Theta oscillations in human memory. Trends in Cognitive Sciences, 24(3), 208–227.

Article  PubMed  PubMed Central  Google Scholar 

Hsiao, Y.-T., Wu, C.-T., Tsai, C.-F., Liu, Y.-H., Trinh, T.-T., & Lee, C.-Y. (2021). Eeg-based classification between individuals with mild cognitive impairment and healthy controls using conformal kernel-based fuzzy support vector machine. International Journal of Fuzzy Systems, 23, 2432–2448.

Article  Google Scholar 

Johannesen, J. K., Bi, J., Jiang, R., Kenney, J. G., & Chen, C.-M.A. (2016). Machine learning identification of eeg features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatric Electrophysiology, 2, 1–21.

Article  Google Scholar 

Kang, T., Chen, Y., Fazli, S., & Wallraven, C. (2020). Eeg-based prediction of successful memory formation during vocabulary learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(11), 2377–2389.

Article  PubMed  Google Scholar 

Kaplan, R., Bush, D., Bonnefond, M., Bandettini, P. A., Barnes, G. R., Doeller, C. F., & Burgess, N. (2014). Medial prefrontal theta phase coupling during spatial memory retrieval. Hippocampus, 24(6), 656–665.

Article  PubMed  PubMed Central  Google Scholar 

Kaplan, R., Doeller, C. F., Barnes, G. R., Litvak, V., Düzel, E., Bandettini, P. A., & Burgess, N. (2012). Movement-related theta rhythm in humans: coordinating self-directed hippocampal learning. PLoS Biology, 10(2), e1001267.

Article  PubMed  PubMed Central  Google Scholar 

Kerrén, C., Linde-Domingo, J., Hanslmayr, S., & Wimber, M. (2018). An optimal oscillatory phase for pattern reactivation during memory retrieval. Current Biology, 28(21), 3383–3392.

Article  PubMed  Google Scholar 

Kiiski, H., Jollans, L., Donnchadha, S. Ó., Nolan, H., Lonergan, R., Kelly, S., O’Brien, M. C., Kinsella, K., Bramham, J., Burke, T., et al. (2018). Machine learning eeg to predict cognitive functioning and processing speed over a 2-year period in multiple sclerosis patients and controls. Brain Topography, 31, 346–363.

Article  PubMed  Google Scholar 

Klimesch, W., Doppelmayr, M., Schimke, H., & Ripper, B. (1997). Theta synchronization and alpha desynchronization in a memory task. Psychophysiology, 34(2), 169–176.

Kunz, L., Schröder, T. N., Lee, H., Montag, C., Lachmann, B., Sariyska, R., Reuter, M., Stirnberg, R., Stöcker, T., Messing-Floeter, P. C., et al. (2015). Reduced grid-cell-like representations in adults at genetic risk for alzheimer’s disease. Science, 350(6259), 430–433.

Article  PubMed  Google Scholar 

Liang, M., Zheng, J., Isham, E., & Ekstrom, A. (2021). Common and distinct roles of frontal midline theta and occipital alpha oscillations in coding temporal intervals and spatial distances. Journal of Cognitive Neuroscience, 33(11), 2311–2327.

Article  PubMed  Google Scholar 

Liang, M., Zheng, J., Isham, E., & Ekstrom, A. (2021). Common and distinct roles of frontal midline theta and occipital alpha oscillations in coding temporal intervals and spatial distances. Journal of Cognitive Neuroscience, 33(11), 2311–2327.

Article  PubMed  Google Scholar 

Lin, J.-J., Rugg, M. D., Das, S., Stein, J., Rizzuto, D. S., Kahana, M. J., & Lega, B. C. (2017). Theta band power increases in the posterior hippocampus predict successful episodic memory encoding in humans. Hippocampus, 27(10), 1040–1053.

Article  PubMed  PubMed Central  Google Scholar 

Longo, L. (2023). Explainable Artificial Intelligence: First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings. Springer Nature: Part II.

Book  Google Scholar 

Mitchell, D. J., McNaughton, N., Flanagan, D., & Kirk, I. J. (2008). Frontal-midline theta from the perspective of hippocampal “theta”. Progress in Neurobiology,86(3), 156–185.

Musaeus, C. S., Engedal, K., Høgh, P., Jelic, V., Mørup, M., Naik, M., Oeksengaard, A.-R., Snaedal, J., Wahlund, L.-O., Waldemar, G., et al. (2018). Eeg theta power is an early marker of cognitive decline in dementia due to alzheimer’s disease. Journal of Alzheimer’s Disease, 64(4), 1359–1371.

Article  PubMed  Google Scholar 

Nirabi, A., Abd Rahman, F., Habaebi, M. H., Sidek, K. A., & Yusoff, S. (2021). Machine learning-based stress level detection from eeg signals. In 2021 IEEE 7th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) (pp. 53–58). IEEE.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135–1144).

Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape,(with discussion). Applied Statistics, 54, 507–554.

Google Scholar 

Roberts, B. M., Hsieh, L.-T., & Ranganath, C. (2013). Oscillatory activity during maintenance of spatial and temporal information in working memory. Neuropsychologia, 51(2), 349–357.

Article  PubMed  Google Scholar 

Stasinopoulos, M. D., Rigby, R. A., Heller, G. Z., Voudouris, V., & Bastiani, F. D. (2017). Flexible regression and smoothing : using GAMLSS in R. R. Chapman and Hall/CRC.

Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M. (2011). Brainstorm: a user-friendly application for meg/eeg analysis. Computational Intelligence and Neuroscience, 2011, 1–13.

Article  Google Scholar 

Tang, C., Li, Y., & Chen, B. (2022). Comparison of cross-subject eeg emotion recognition algorithms in the bci controlled robot contest in world robot contest 2021. Brain Science Advances, 8(2), 142–152.

Article  Google Scholar 

Thornberry, C., Caffrey, M., & Commins, S. (2023). Theta oscillatory power decreases in humans are associated with spatial learning in a virtual water maze task. European Journal of Neuroscience, 58(11), 4341–4356.

Vahid, A., Mückschel, M., Neuhaus, A., Stock, A.-K., & Beste, C. (2018). Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses. Scientific Reports, 8(1), 16235.

Article  PubMed  PubMed Central  Google Scholar 

Visser, I., & Speekenbrink, M. (2010). depmixS4: An R package for hidden markov models. Journal of Statistical Software, 36(7), 1–21.

Zucchini, W., & MacDonald, I. L. (2016). Hidden Markov models for time series: an introduction using R. Chapman and Hall/CRC, second edition.

Żygierewicz, J., Janik, R. A., Podolak, I. T., Drozd, A., Malinowska, U., Poziomska, M., Wojciechowski, J., Ogniewski, P., Niedbalski, P., Terczynska, I., et al. (2022). Decoding working memory-related information from repeated psychophysiological eeg experiments using convolutional and contrastive neural networks. Journal of Neural Engineering, 19(4), 046053.