Jiahua Xu
Tel.
Position
Postdoc
Affiliations
Hertie Institute for Clinical Brain Research, Tübingen, Germany
Research Interests
My research centers on AI-driven closed-loop brain stimulation, which integrates real-time decoding of brain oscillations (e.g., EEG) with non-invasive neuromodulation techniques such as transcranial electrical or magnetic stimulation, guided by machine learning algorithms
Research Groups
Precision Brain Stimulation
Brain Network Modulation group
Movement Disorders group
EEG group
Curriculum Vitae
Education
2022
PhD in Computer Science, University Magdeburg
2013
MSc in Computer Science, University Macau
2009
BSc in Electrical Engineering, Macau University of Science and Technology
Employments
2025 - Present
Postdoc at DRCMR
2023 - Present
Visiting Scholar at Aalto University
2022 - Present
Postdoc at Universitätsklinikum Tübingen
2016 - 2022
Research Assistant at Universitätsklinikum Magdeburg
Publications
Humaidan, D. *, Xu, J. *, Kirchhoff, M., Romani, G. L., Ilmoniemi, R. J., & Ziemann, U. (2024). Towards real-time EEG–TMS modulation of brain state in a closed-loop approach. Clinical Neurophysiology, 158, 212-217(*equal contribution).
Wu, Z., Xu, J., Nürnberger, A., & Sabel, B. A. (2023). Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study. Cerebral Cortex, 33(8), 4729-4739.
Xu, J., Wu, Z., Nürnberger, A., & Sabel, B. A. (2021). Reorganization of brain functional connectivity network and vision restoration following combined tACS-tDCS treatment after occipital stroke. Frontiers in Neurology, 12, 729703.
Xu, J., Schoenfeld, M. A., Rossini, P. M., Tatlisumak, T., Nürnberger, A., Antal, A., ... & Sabel, B. A. (2022). Adaptive and maladaptive brain functional network reorganization after stroke in hemianopia patients: An electroencephalogram-tracking study. Brain Connectivity, 12(8), 725-739.
Xu, J., Zheng, W., Nürnberger, A., & Sabel, B. A. (2021, September). Predicting Brain Electrical Stimulation Outcome in Stroke by Clinical-inspired Hybrid Graph Convolutional Autoencoder. In 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS) (pp. 1-3). IEEE.
Xu, J., Wu, Z., Nürnberger, A., & Sabel, B. A. (2021, November). Interhemispheric Cortical Network Connectivity Reorganization Predicts Vision Impairment in Stroke. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 836-840). IEEE.
Xu, J., Wu, Z., Nürnberger, A., & Sabel, B. A. (2021, May). Decoding resting-state eeg to predict visual field defect with convolutional neural network in stroke. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 807-810). IEEE.
Xu, J., Ernst, P., Liu, T. L., & Nürnberger, A. (2021, November). Dual skip connections minimize the false positive rate of lung nodule detection in ct images. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3217-3220). IEEE.
Xu, J. H., Teng, W. P. K., Wang, X. J., & Nürnberger, A. (2021). A deep supervised U-attention net for pixel-wise brain tumor segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II 6 (pp. 278-289). Springer International Publishing.
Xu, J., Liu, T. L., Wu, Z., Wu, Z., Li, Y., & Nürnberger, A. (2020, September). Neurorehabilitation system in virtual reality with low-cost BCI devices. In 2020 IEEE International Conference on Human-Machine Systems (ICHMS) (pp. 1-3). IEEE.