Zohreh Doborjeh

Zohreh is a Research Fellow/Lecturer at AUT and has been principally researching in the field of Neuroinformatics and Cognitive Computational Neuroscience. She is in charge of EEGlab at AUT and leads several projects for applying AI technology to improve decision-making in psychology-based applications, specifically in mental health and wellbeing. Zohreh received her B.S and M.S. with honors in psychology and PhD in Neuroinformatics at AUT. Her PhD research was about applying advanced analytical methods to different psychological-based experiments that include (1) understanding of human preferences, emotions and cognitive biases in marketing environment; and (2) evaluation of the effects of mindfulness intervention on behavioural responses and cognitive abilities in university and workplace settings. Her work has contributed to her being awarded the Dean’s Award of Excellence, Best PhD student and three Best International Paper Awards at AUT.

Links:

Qualifications:

  • PhD in Neuroinformatics (KERDI, Auckland University of Technology, New Zealand, 2015-2019)
  • M.S. in Psychology (honour student), Ferdowsi University of Mashhad, Iran, 2014.
  • B.S in Psychology (honour student), Ferdowsi University of Mashhad, Iran, 2011).

Prizes or Scholarships:

  • 2019, Best Journal Paper Award, AUT.
  • 2018, Dean’s Award of Excellence for outstanding performance in postgraduate studies, AUT.
  • 2018, Best Journal Paper Award, AUT.
  • 2017, Best Journal Paper Award, AUT.
  • 2016, Summer Research Scholarship, AUT.
  • 2016, Summer Research Scholarship, AUT
  • 2015-2018, PhD Fee Scholarship Award from Knowledge Engineering and Discovery Research Institute, AUT.
  • 2016, Conference Grant Award to attend ICONIP Conference in Japan, AUT.
  • 2016, Conference Grant Award to attend IJCNN Conference in Canada, AUT.

Research area:

  • Neuromarketing.
  • Mental health and wellbeing.
  • Computational Cognitive Neuroscience.
  • Dynamic Spatio-temporal brain data analysis.
  • Pattern recognition, event prediction and comparative analysis between different mental states using advanced brain inspired-AI techniques.

Academic Projects:

  • Mindfulness project, as a SRIF project funded by Auckland University of Technology, New Zealand (2016-2017)
  • Cross-university collaboration project on microsleep study with Canterbury University, Christchurch, New Zealand (2016-2017)
  • JSPS-RSNZ project on mirror neuron system and ASD (2017-2018)

Teaching area:

  • Neuroinformatics

Memberships:

  • AUT Artificial Intelligence Initiative.
  • IEEE member.
  • AAN (American Academy of neurology).
  • APNNS (Asia Pacific Neural Network Society).
  • Neurological Foundation of New Zealand.
  • New Zealand Tertiary Education Union.

Journal Publications:

  1. Merkin, A. G., Medvedev, O. N., Sachdev, P. S., Tippett, L., Krishnamurthi, R., Mahon, S., ... & Doborjeh, Z. . (2019). New avenue for the Geriatric Depression Scale: Rasch transformation enhances reliability of assessment. Journal of Affective Disorders.
  2. Doborjeh, M., Kasabov, N., Doborjeh, Z., Enayatollahi, R., Tu, E., & Gandomi, A. H. (2019). Personalised modelling with spiking neural networks integrating temporal and static information. Neural Networks, 119, 162. https://www.sciencedirect.com/science/article/pii/S0893608019302175
  3. Doborjeh, Z., Doborjeh, M., Crook-Rumsey, M., Moreau, D., Taylor, T., Wang, G. Y., Krageloh, Ch., Wrapson, W., Kasabov, N., Siegert, R., & Sumich, A. (2019). Dynamic Pattern Recognition of Spatiotemporal Brain Data Following a Mindfulness Intervention. Nature Neuroscience, submitted.
  4. Doborjeh, Z., Doborjeh, M., Taylor, T., Kasabov, N., Wang, G. Y., Siegert, R., & Sumich, A. (2019). Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain. Nature Scientific reports, 9(1), 6367. https://www.nature.com/articles/s41598-019-42863-x.
  5. Doborjeh, Z., Kasabov, N., Doborjeh, M. G., & Sumich, A. (2018). Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture. Nature Scientific reports, 8(1), 8912. https://www.nature.com/articles/s41598-018-27169-8
  6. Doborjeh, Z., Doborjeh, M. G., & Kasabov, N. (2018). Attentional bias pattern recognition in spiking neural networks from spatio-temporal EEG data. Cognitive Computation, 10(1), 35-48. https://link.springer.com/article/10.1007/s12559-017-9517-x
  7. Kasabov, N. K., Doborjeh, M., & Doborjeh, Z. (2017). Mapping, learning, visualization, classification, and understanding of fMRI Data in the NeuCube evolving spatiotemporal data machine of spiking neural networks. IEEE transactions on neural networks and learning systems, 28(4), 887-899. https://www.ncbi.nlm.nih.gov/pubmed/27723607
  8. Doborjeh, M., Kasabov, N., & Doborjeh, Z. (2017). Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data. Evolving systems, 1-17. https://link.springer.com/article/10.1007/s12530-017-9178-8
  9. Kasabov, N., Zhou, L., Doborjeh, M., Doborjeh, Z., & Yang, J. (2017). New Algorithms for Encoding, Learning and Classification of fMRI Data in a Spiking Neural Network Architecture: A Case on Modeling and Understanding of Dynamic Cognitive Processes. IEEE Transactions on Cognitive and Developmental Systems, 9(4), 293-303. https://ieeexplore.ieee.org/document/7776755/

Conference Proceedings:

  1. Shah, D., Wang, G., Doborjeh, M., Doborjeh. Z., Kasabov, N. (2019) Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression. Springer Nature, ICONIP. https://doi.org/10.1007/978-3-030-36718-3_17.
  2. Doborjeh, Z., Doborjeh, M. G, Taylor, T., Kasabov, N., Wang, G., Siegert, R., Sumich, A. (2019). Understanding the Creation of Functional Pathways through Mindfulness Intervention based on Brain-Inspired Spiking Neural Networks. Abstract in ICM conference, New Zealand.
  3. Doborjeh, Z., Doborjeh, M., Kasabov, N. (2018). EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  4. Doborjeh, Z., Doborjeh, M., Kasabov, N. (2017) .Efficient Recognition of Attentional Bias using EEG data and the NeuCube Evolving Spatio-Temporal Data Machine”, ICONIP in Kyoto, vol. 9950, pp. 645-653. https://link.springer.com/chapter/10.1007/978-3-319-46681-1_76
  5. Omori, Y., Kawano, H., Seo, A., Doborjeh, Z., Kasabov, N. and Doborjeh, M., 2017, November. EEG Comparison between Normal and Developmental Disorder in Perception and Imitation of Facial Expressions with the NeuCube. In International Conference on Neural Information Processing (pp. 596-601). Springer, Cham.
  6. Capecci, E., Doborjeh, Z., Mammone, N., La Foresta, F., Morabito, F. C., & Kasabov, N. (2016, July). Longitudinal study of Alzheimer’s disease degeneration through EEG data analysis with a NeuCube spiking neural network model. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 1360-1366). IEEE. https://ieeexplore.ieee.org/document/7727356/
  7. Kawano, H., Seo, A., Doborjeh, Z., Kasabov, N., & Doborjeh, M. G. (2016). Analysis of similarity and differences in brain activities between perception and production of facial expressions using EEG DATA and the NeuCube spiking neural network architecture. In International Conference on Neural Information Processing (pp. 221-227). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-46681-1_27
  8. Doborjeh, Z., Doborjeh, M., Kasabov, N., Wang, G.   (2015) ERP Evidence for Prediction, classification, and visualisation of the Consumers’ Preferences to Marketing Logos in Neuromarketing”, Abstract in NCEI, New Zealand.

Book Chapter:

  1. Doborjeh, M.G., Doborjeh, Z., Gollahalli, A.R., Kumarasinghe, K., Breen, V.,   Sengupta, N., Ramos, J.I.E., Hartono, R., Capecci, E., Kawano, H. and Othman,   M., (2018). From von Neumann Architecture and Atanasoff’s ABC to Neuromorphic   Computation and Kasabov’s NeuCube. Part II: Applications. In Practical Issues   of Intelligent Innovations (pp. 17-36). Springer, Cham.
  2. Sengupta, N., Ramos, J.I.E., Tu, E., Marks, S.,   Scott, N., Weclawski, J., Gollahalli, A.R., Doborjeh, M., Doborjeh, Z., Kumarasinghe, K. and   Breen, V., (2018). From von Neumann Architecture and Atanasoffs ABC to   Neuro-Morphic Computation and Kasabov’s NeuCube: Principles and   Implementations. In Learning Systems: From Theory to Practice (pp. 1-28). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-75181-8_1

Abstract and Poster:

  1. Doborjeh, Z., Doborjeh, M., Taylor, T., Kasabov, N., Wang, G., Siegert, R., Sumich, A. (2019). Understanding the Creation of Functional Pathways through Mindfulness Intervention based on Brain-Inspired Spiking Neural Networks. Abstract in ICM conference, New Zealand.
  2. Doborjeh, Z., Doborjeh, M., Kasabov, N., Wang, G. (2015) ERP Evidence for Prediction, classification, and visualisation of the Consumers’ Preferences to Marketing Logos in Neuromarketing”, Abstract in NCEI, New Zealand

PhD Thesis:

  1. Doborjeh, Z. (2019). Modelling of Spatiotemporal EEG and ERP Brain Data for Dynamic Pattern Recognition and Brain State Prediction using Spiking Neural Networks: Methods and Applications in Psychology (Doctoral dissertation, Auckland University of Technology).