Dr Matthias Treder

Cynadleddau

• Zhang, M., Treder, M., Marshall, A. and Li, Y. 2024. Fast explanation of RBF-Kernel SVM models using activation patterns. Presented at: International Joint Conference on Neural Networks, Yokohama, Japan, 30 June – 5 July 2024Proceedings of IJCNN. IEEE pp. 1-8., (10.1109/IJCNN60899.2024.10650697)
• Krzeminski, D., Michelmann, S., Treder, M. and Santamaria, L. 2019. Classification of P300 component using a riemannian ensemble approach. Presented at: MEDICON 2019XV Mediterranean Conference on Medical and Biological Engineering and Computing, Coimbra, Portugal, 26-28 September 2019, Vol. 76. Springer Science Business Media, (10.1007/978-3-030-31635-8_229)
• Treder, M. 2019. Direct calculation of out-of-sample predictions in multi-class kernel FDA. Presented at: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 24-26 April 2019ESANN 2019 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN pp. 245-250.
• Treder, M. S. 2018. Improving SNR and reducing training time of classifiers in large datasets via kernel averaging. Presented at: BI 2018, Arlington, TX, USA, 7-9 Dec 2018 Presented at Wang, S. et al. eds.Brain Informatics, Vol. 2018. Lecture Notes in Computer Science Cham, Switzerland: Springer Verlag pp. 239-248., (10.1007/978-3-030-05587-5_23)

Erthyglau

• Zhang, M., Treder, M., Marshall, D. and Li, Y. 2024. Explaining the predictions of kernel SVM models for neuroimaging data analysis. Expert Systems with Applications 251, article number: 123993. (10.1016/j.eswa.2024.123993)
• Treder, M., Lee, S. and Tsvetanov, K. 2024. Introduction to Large Language Models (LLMs) for dementia care and research. Frontiers in Dementia 3, article number: 1385303. (10.3389/frdem.2024.1385303)
• Wang, H., Treder, M., Marshall, D., Jones, D. and Li, Y. 2023. A skewed loss function for correcting predictive bias in brain age prediction. IEEE Transactions on Medical Imaging 42(6), pp. 1577-1589. (10.1109/TMI.2022.3231730)
• Abdellahi, M., Koopman, A., Treder, M. and Lewis, P. 2023. Targeted memory reactivation in human REM sleep elicits detectable reactivation. eLife 12, article number: e84324. (10.7554/elife.84324)
• Abdellahi, M. E. A., Koopman, A. C. M., Treder, M. S. and Lewis, P. A. 2023. Targeting targeted memory reactivation: characteristics of cued reactivation in sleep. NeuroImage 266, article number: 119820. (10.1016/j.neuroimage.2022.119820)
• Navarrete, M., Arthur, S., Treder, M. S. and Lewis, P. A. 2022. Ongoing neural oscillations predict the post-stimulus outcome of closed loop auditory stimulation during slow-wave sleep. NeuroImage 253, article number: 119055. (10.1016/j.neuroimage.2022.119055)
• Plumley, A., Watkins, L., Treder, M., Liebig, P., Murphy, K. and Kopanoglu, E. 2022. Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design. Magnetic Resonance in Medicine 87(5), pp. 2254-2270. (10.1002/mrm.29132)
• Chopard, D., Treder, M., Corcoran, P., Johnson, C., Busse-Morris, M. and Spasic, I. 2021. Text mining of adverse events in clinical trials: Deep learning approach. JMIR Medical Informatics 9(12), article number: e28632. (10.2196/28632)
• Treder, M. S. et al. 2021. The hippocampus as the switchboard between perception and memory. Proceedings of the National Academy of Sciences 118(50), article number: e2114171118. (10.1073/pnas.2114171118)
• Treder, M. S., Shock, J. P., Stein, D. J., du Plessis, S., Seedat, S. and Tsvetanov, K. A. 2021. Correlation constraints for regression models: controlling bias in brain age prediction. Frontiers in Psychiatry 12, article number: 615754. (10.3389/fpsyt.2021.615754)
• Treder, M. S. 2020. MVPA-Light: a classification and regression toolbox for multi-dimensional data. Frontiers in Neuroscience 14, article number: 289. (10.3389/fnins.2020.00289)
• Linde-Domingo, J., Treder, M. S., Kerren, C. and Wimber, M. 2019. Evidence that neural information flow is reversed between object perception and object reconstruction from memory. Nature Communications 10, article number: 179. (10.1038/s41467-018-08080-2)
• Tsvetanov, K. A. et al. 2018. Activity and connectivity differences underlying inhibitory control across the adult life span. Journal of Neuroscience 38(36), pp. 7887-7900. (10.1523/JNEUROSCI.2919-17.2018)
• Michelmann, S. et al. 2018. Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA). Journal of Neuroscience Methods 307, pp. 125-137. (10.1016/j.jneumeth.2018.06.021)
• Ioannidis, K. et al. 2018. Problematic intemet use as an age-related multifaceted problem: Evidence from a two-site survey. Addictive Behaviors 81, pp. 157-166. (10.1016/j.addbeh.2018.02.017)
• Price, D. et al. 2017. Age-related delay in visual and auditory evoked responses is mediated by white- and grey-matter differences. Nature Communications 8, article number: 15671. (10.1038/ncomms15671)
• Samu, D. et al. 2017. Preserved cognitive functions with age are determined by domain-dependent shifts in network responsivity. Nature Communications 8, article number: 14743. (10.1038/ncomms14743)
• Ioannidis, K. et al. 2016. Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research 83, pp. 94-102. (10.1016/j.jpsychires.2016.08.010)
• Wolpe, N. et al. 2016. Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. Nature Communications 7, article number: 13034. (10.1038/ncomms13034)
• Henson, R. N. et al. 2016. Multiple determinants of lifespan memory differences. Scientific Reports 6, article number: 32527. (10.1038/srep32527)
• Treder, M. S., Porbadnigk, A. K., Avarvand, F. S., Mueller, K. and Blankertz, B. 2016. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis. NeuroImage 129, pp. 279-291. (10.1016/j.neuroimage.2016.01.019)
• Hwang, H., Ferreria, V. Y., Ulrich, D., Kilic, T., Chatziliadis, X., Blankertz, B. and Treder, M. 2015. A gaze independent brain-computer interface based on visual stimulation through closed eyelids. Scientific Reports 5, article number: 15890. (10.1038/srep15890)
• Treder, M. S., Purwins, H., Miklody, D., Sturm, I. and Blankertz, B. 2014. Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. Journal of Neural Engineering 11(2), article number: 26009. (10.1088/1741-2560/11/2/026009)
• Sonnleitner, A., Treder, M. S., Simon, M., Willmann, S., Ewald, A., Buchner, A. and Schrauf, M. 2014. EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study. Accident Analysis and Prevention 62, pp. 110-118. (10.1016/j.aap.2013.08.026)

Gwefannau

• Treder, M., Mayor-Torres, J. and Teufel, C. 2020. Deriving visual semantics from spatial context: an adaptation of LSA and Word2Vec to generate object and scene embeddings from images. [Online]. Cornell University. Available at: https://arxiv.org/abs/2009.09384
• Treder, M. S. 2018. Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA. [Online]. arXiv. Available at: http://arxiv.org/abs/1803.10016

• Tsvetanov, K. A. et al. 2018. Activity and connectivity differences underlying inhibitory control across the adult life span. Journal of Neuroscience 38(36), pp. 7887-7900. (10.1523/JNEUROSCI.2919-17.2018)
• Michelmann, S. et al. 2018. Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA). Journal of Neuroscience Methods 307, pp. 125-137. (10.1016/j.jneumeth.2018.06.021)
• Price, D. et al. 2017. Age-related delay in visual and auditory evoked responses is mediated by white- and grey-matter differences. Nature Communications 8, article number: 15671. (10.1038/ncomms15671)
• Samu, D. et al. 2017. Preserved cognitive functions with age are determined by domain-dependent shifts in network responsivity. Nature Communications 8, article number: 14743. (10.1038/ncomms14743)
• Ioannidis, K. et al. 2016. Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research 83, pp. 94-102. (10.1016/j.jpsychires.2016.08.010)
• Wolpe, N. et al. 2016. Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. Nature Communications 7, article number: 13034. (10.1038/ncomms13034)
• Henson, R. N. et al. 2016. Multiple determinants of lifespan memory differences. Scientific Reports 6, article number: 32527. (10.1038/srep32527)