Academic Title: Associate Professor, Master’s Supervisor

Affiliated Institution: Institute of Information and Communication Engineering

Office Address: Room 303, Feiyun Building

Educational Background

2017.09-2022.08 Doctor of Biomedical Science, Biomedical Engineering, KU Leuven, Belgium

2011.09-2014.06 Master of Engineering in Electronic and Communication Engineering, Lanzhou University

2007.08-2011.06 Bachelor of Engineering in Communication Engineering, Lanzhou University

Professional Experience

2023.07-Present Associate Professor, School of Information Science and Engineering, Lanzhou University

Teaching Experience

Bioelectrical Signal Processing, Electroencephalography (EEG) and Mobile EEG, Mobile Brain-Body Imaging, EEG-based Neurofeedback for Brain Function Regulation

Research Interests

Bioelectrical Signal Processing, Electroencephalography (EEG) and Mobile EEG, Mobile Brain-Body Imaging, EEG-based Neurofeedback for Brain Function Regulation

Publications (Papers and Books)

17 SCI/EI-indexed papers published. Selected papers in the past 5 years are as follows:

1. Journal Papers

[1] Zhao M., Bonassi G., Samogin J., Taberna G.A., Pelosin E., Nieuwboer A., Avanzino L., & Mantini D.^* (accepted). Assessing neuromuscular and neurokinematic connectivity during walking using mobile brain-body imaging. Frontiers in Neuroscience.

[2] Zhao M., Bonassi G., Samogin J., Taberna G., Pelosin E., Nieuwboer A., Avanzino L., & Mantini D.* (2022). Frequency-dependent modulation of neural oscillations across the gait cycle. Human Brain Mapping. 43(11), 3404–3415.

[3] Zhao M., Bonassi G., Guarnieri R., Pelosin E., Nieuwboer A., Avanzino L., & Mantini D.* (2021). A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data. Journal of Neural Engineering. 18(6), 066041.

[4] Zhang H.#, Zhao M.#, Wei C., Mantini D., Li Z., & Liu Q.* (2021). EEEGdenoisenet: A benchmark dataset for deep learning solutions of EEG denoising. Journal of Neural Engineering, 18(5), 056057.

[5] Zhao M., Marino M., Samogin J., Swinnen S. P., & Mantini D.* (2019). Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study. Scientific Reports, 9(1), 1-12.

[6] Zhang H., Zhang K., Zhang Z., Zhao M., Liu Q., Luo W., & Wu H.^* (2021). Inter-trial variations in EEG predict the individual differences in social tasks. BioRxiv.

[7] Guarnieri R., Zhao M., Taberna G. A., Ganzetti M., Swinnen S. P., & Mantini D.* (2021). RT-NET: real-time reconstruction of neural activity using high-density electroencephalography. Neuroinformatics, 19(2), 251-266.

[8] Iandolo R., Semprini M., Buccelli S., Barban F., Zhao M., Samogin J., … & Chiappalone M.^* (2020). Small-world propensity reveals the frequency specificity of resting state networks. IEEE Open Journal of Engineering in Medicine and Biology, 1, 57-64.

[9] Liu X., Zhang R.^*, & Zhao M. (2019). A robust authentication scheme with dynamic password for wireless body area networks. Computer Networks, 161, 220-234.

[10] Botta A., Zhao M., Samogin J., Pelosin E., Bonassi G., Lagravinese G., Mantini D., Avenanti A., Avanzino L.^* (under review). Early modulations of neural oscillations during processing of emotional body language. Psychophysiology.

[11] Taberna G., Samogin J., Zhao M., Marino M., Guarnieri R., Morales C. E., Ganzetti M., Liu Q., Mantini D.* (under review 2023). NET: open-source software package for large-scale analysis of neural activity and connectivity from high-density electroencephalography data. GigaScience.

[12] Zhang H., Zhang K., Zhang, Z., Zhao M., Liu Q., Luo W., & Wu H. (2023). Social conformity is associated with inter-trial electroencephalogram variability. Ann NY Acad Sci.,1523,104–118.

[13] Bossche L. V., Schaeffer M., Langer D., Zhao M., Mantini D., Janssens W., … & von Leupoldt, A. (2022). Neural gating during exercise-induced dyspnea. OSF preprint.

[14] Botta A., Zhao M., Samogin J., Bonassi G., Lagravinese G., Terranova S., … & Avanzino L. (2023). Early modulations of neural oscillations during processing of emotional body language. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 16(1), 385.

2. Conference Papers

[1] Zhang H., Wei C., Zhao M., Liu Q.^*, & Wu H. (2021, June). A novel convolutional neural network model to remove muscle artifacts from EEG. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1265-1269). IEEE.

[2] Wei C., Lou K., Wang Z., Zhao M., Mantini D., & Liu Q.^* (2021, July). Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization. In the International Joint Conference on Neural Networks (IJCNN).

3. Conference Abstract

[1] Zhao M., Marino M., Samogin J., Swinnen S. P., & Mantini D.^* (2019, October). Mapping motor-related modulations of neural activity using high-density electroencephalography. In Organization of Human Brain Mapping Conference (OHBM).

[2] Guarnieri R., Zhao M., Taberna G. A., Ganzetti M., Swinnen S. P., & Mantini D.^* (2019, October). Real-time estimation of brain activity from high density EEG. In Organization of Human Brain Mapping Conference (OHBM).

[3] Guarnieri R., Zhao M., Taberna G. A., Ganzetti M., Swinnen S. P., & Mantini D.^* (2020, October). RT-NET: a software package for neural activity estimation from high-density EEG recordings. In Organization of Human Brain Mapping Conference (OHBM).