Real-World NeuroImaging Research and BCI:

From Research to Reality

Updated: 3:45pm, 30 Nov, 2022
24 April 2018 (Tue)
Room 101, 1/F., Runme Shaw Bldg., HKU
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Chair: Prof. Nancy LAW, Professor, Faculty of Education, The University of Hong Kong

The past twenty years have witnessed remarkable advances in both fundamental neuroscience research and next-generation neurotechnologies. Nonetheless, nearly all brain imaging research to date has required participants to remain nearly motionless and perform improvised stimulus-response experiments while their brain activity is recorded within a well-controlled laboratory setting. It has been argued that fundamental differences between laboratory-based and naturalistic human behavior may exist. It remains unclear how well the current knowledge of human brain function translates into the highly dynamic real world [1]. Therefore, there is a need to study the brain in ecologically valid environments in order to truly understand how the human brain functions to optimally control behavior in face of ever-changing physical and cognitive circumstances. This talk will focus on recent progress in real-world neuroimaging and Brain-Computer Interfaces that leverages innovations advanced by our groups at University of California San Diego and National Chiao Tung University [2-4].  This talk will also explore the effects of real-life stressors (e.g. sleep quality and stress) on the EEG activities in the classroom and other real-world environments. [5-7].


  1. McDowell K, Lin C-T., Oie K.S., Jung T-P., Gordon S., Whitaker K.W., Li S-Y., Lu S-W., Hairston W-D., Real-World Neuroimaging Technologies,” IEEE Access, 1:131-49, 2013.
  2. Siddharth, S., Jung, T-P., Sejnowski, T.J. “How about taking a Low-cost Multimodal Bio-sensing and Eye-gaze Tracking System into the “Wild”? Proceedings of the 38th International IEEE EMBS conference, 2016.
  3. Liao, L-D., Lin, C-T.,  McDowell, K., Wickenden, A. E., Gramann, K., Jung, T-P. Ko, L-W., Chang, J-Y. Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades, Proceedings of the IEEE, 100:1553-66, 2012.
  4. Zao, J.K., Gan,T-T., You, C-K., Rodríguez_Méndez, S.C., Chung, C-E., Wang, Y-T., Mullen, T., Kothe, C., Yu, C., Hsiao, C-T., Chu, S., Shieh, C-K., Jung, T-P., Pervasive Brain Monitoring and Data Sharing based on Multi-tier Distributed Computing and Linked Data Technology, Frontiers in Human Neuroscience, 2014 Jun 3;8:370. doi: 10.3389/fnhum.2014.00370.
  5. Chen, S.-C., She, H.-C., Chuang, M.-H., Wu, J.-Y., Tsai, J.-L., Jung, T.-P. Eye movements predict students' computer-based assessment performance of physics concepts in different presentation modalities, Computers & Education, 74: 61–72, 2014.
  6. Chou, W.C., She, H. C., Lai, K., Gramann, K., & Jung, T. P. Temporal Dynamics and Cortical Networks Engaged in Biological Concepts Encoding. Journal of Neuroscience and Neuroengineering, 3(1), 21–35, 2014.
  7. Ko, L-W., Komarov, O., Hairston, W.D., Jung, T-P., and Lin, C-T., Sustained Attention in Real Classroom Settings: An EEG Study, Frontiers in Human Neuroscience, 11:article 388, 2017.
About the speaker(s):

Tzyy-Ping Jung is currently the Co-Director of Center for Advanced Neurological Engineering, Associate Director of the Swartz Center for Computational Neuroscience and an Adjunct Professor of Department of Bioengineering at University of California San Diego, CA, USA. He is also an Adjunct Professor of Department of Electrical Engineering and Department of Computer Science at National Chiao Tung University, Hsinchu, Taiwan. In addition, he is an Adjunct Professor at School of Precision Instrument and Opto-electronic Engineering, Tianjin University, Tianjin, China.

Dr. Jung established transformative techniques for applying blind source separation to decompose multichannel EEG/MEG/ERP and fMRI data and was elevated to an IEEE Fellow for his contributions to blind source separation for biomedical applications in 2015. Dr. Jung has also developed wearable sensing systems with dry and non-prep bio-potential sensors, online real-time signal processing and artifact correction on smartphones or mobile devices. The revolutionary design allows assessment of neural, physiological and behavioral data of participants actively performing ordinary tasks in natural body positions and situations in real-world environments. Dr. Jung’s work is truly interdisciplinary and well cited by peers (~21,000 total citations, h-index = 56 according to Google Scholar).

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