Zohreh Doborjeh

Zohreh Doborjeh is a PhD student and Research Assistant at KEDRI, under the supervision of Prof. Nikola Kasabov, Dr Grace Wang (AUT, psychology department) and Dr Alex Sumich (Nottingham University, UK. psychology department).  Her topic of study is the “dynamic modelling of spatio-temporal brain data with spiking neural networks for the study of human brain dynamics and human behaviour”. Zohreh is in charge of KEDRI’s EEGLAB which supports recording and on-line tracking of EEG spatio-temporal brain data for the purpose of understanding brain processes under different conditions and for different studies.  Zohreh obtained an MS. degree (as honour student) in psychology, from Ferdowsi University, Iran, in 2013. She has previous research experiences in Neuromarketing, Emotion pattern recognition, Cognitive task designing, EEG and ERP data analysis.

Links:

Qualifications:

  • PhD Student (KERDI, Auckland University of Technology, New Zealand, 2015-Present)
  • MS. in   Psychology (honour student), Ferdowsi University of Mashhad, Iran, 2013.
  • Bachelor in Psychology (honour student), Ferdowsi University of Mashhad, Iran, 2011).

Prizes or Scholarships:

  • Knowledge Engineering and Discovery Research Institute Fee Scholarship Holder, AUT University in 2016 and 2017
  • AUT summer research scholarship 2014.
  • AUT summer research scholarship 2015.
  • First rank student in general psychology department of Ferdowsi University– Iran, 2011.

Research area:

  • Dynamic Spatio-temporal brain data analysis
  • Pattern recognition, event prediction and comparative analysis between different brain states
  • Spatiotemporal brain data analysis using NeuCube, Curry software, EEGLAB, Neuroguide
  • Cognitive and behavioural neuroscience and neuropsychology (brain function in mental process)
  • EEG (Electroencephalography) and ERP (event-related potential) brain signals measuring
  • Neuromarketing

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:

  • IEEE, since 2014
  • American Academy of Neurology
  • APNNS (Asia Pacific Neural Network Society)

Journal Publications:

  1. Doborjeh, Z. G., Kasabov, N., Doborjeh, M. G., & Sumich, A. (2018). Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture. Nature Scientific reports8(1), 8912. https://www.nature.com/articles/s41598-018-27169-8
  2. Doborjeh, Z. G., Doborjeh, M. G., & Kasabov, N. (2018). Attentional bias pattern recognition in spiking neural networks from spatio-temporal EEG data. Cognitive Computation10(1), 35-48. https://link.springer.com/article/10.1007/s12559-017-9517-x
  3. Kasabov, N. K., Doborjeh, M. G., & Doborjeh, Z. G. (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 systems28(4), 887-899. https://www.ncbi.nlm.nih.gov/pubmed/27723607
  4. Doborjeh, M. G., Kasabov, N., & Doborjeh, Z. G. (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
  5. Kasabov, N., Zhou, L., Doborjeh, M. G., Doborjeh, Z. G., & 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 Systems9(4), 293-303. https://ieeexplore.ieee.org/document/7776755/

Conference Proceedings:

  1. Doborjeh, G, Z., Doborjeh, G, M., Kasabov, N. (2018). EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes. IEEE WCCI, IJCNN conference in Rio, Brazil.
  2. Capecci, E., Doborjeh, Z. G., 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/
  3. Doborjeh G, Z., Doborjeh, M., Kasabov, N. (2016) .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
  4. Kawano, H., Seo, A., Doborjeh, Z. G., 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

Book Chapter:

  1. Sengupta, N., Ramos, J.I.E., Tu, E., Marks, S., Scott, N., Weclawski, J., Gollahalli, A.R., Doborjeh, M.G., Doborjeh, Z.G., 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.G, Doborjeh, M.G, 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.