Brain Engineering via Neuroimaging Modalities

Investigating brain functions measured by various neuroimaging modalities such as functional MRI (fMRI) and electroencephalography (EEG) toward potential applications such as brain health care 
systems including brain-computer interface (BCI) and brain-machine interface (BMI) to enhance human performance.

 Our goal is to investigate brain functions measured via various neuroimaging modalities including functional MRI (fMRI) and electroencephalography (EEG) employing various signal processing techniques. We have done some interesting works including the fMRI data analyses using a novel analytical methods of independent vector analysis (IVA) and iterative dual-regression with a sparse prior to better estimate true neuronal activity, simultaneous EEG-fMRI method toward sleep and epilepsy study, and real-time fMRI based neurofeedback method. Based on correct understanding of human brain functions, we would like to focus on the brain engineering including the BCI/BMI and ultimately on preclinical applications to develop an option to diagnose and treat the various neuropsychiatric illnesses such as depression, schizophrenia, and substance abuse. We believe that the proper analytical methods to exploit the hidden information of the neuroimaging data would lead to better understanding of the human brain and to better engineer the brain and ultimately toward enhancement of quality of life.

Now, the Question is: 
Brain engineering and brain healthcare applications as well as enhancement of human performance via neuroimaging modalities are possible?


Released Toolbox

Recursive approach of EEG-segment-based principal component analysis (rsPCA) toolbox has been released (click here)
 - Version 1.0: Working on MATLAB framework

Iterative dual-regression with sparse prior (IDRwSP) toolbox has been released on NITRC (click here)
 - Version 1.0: Working on MATLAB framework


Unsupervised learning ICA pioneer award, SPIE DSS, Baltimore, Maryland, USA, May 7-9, 2014, 
Awardee: Jong-Hwan Lee
Title: Unsupervised learning toward brain imaging data analysis

Best Poster Award, Brain Engineering Society of Korea (BESK), 2014
Awardee: Hojung Kang

Recent Publications

Kim HC, Yoo SS, Lee JH, Recursive approach of EEG segment based principal component analysis substantially reduces helium-pump artifacts of EEG data simultaneously acquired with fMRI, NeuroImage, 2015, 104: 437-51.

Kim DY, Lee JH, Non-resting-state fMRI data analysis fMRI data analysis via default-mode networks: Feasibility study, the Fourth Biennial Conference on Resting State / Brain Connectivity, Boston, USA

Lee JH, Kim HC, Zhang Z, Lee SY, Neural bases of human strategic reasoning from functional MRI study, OHBM 2014, Hamburg, Germany

Upcoming Conferences & Workshops

- 5th International Workshop onPattern Recognition in Neuroimaging (PRNI 2015), June 10-12, 2015, Stanford University, USA (https://sites.google.com/site/prni2015/.

- Organization for Human Brain Mapping 2015 (OHBM 2015), June 14-18, 2015, Honolulu, Hawaii, USA (http://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=1)
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Posts in the Board

  • [News] Answering personal questions activates unique brain activity pattern, study suggests Brett Foster (a postdoctoral research fellow at Stanford University, told FoxNews.com) and colleagues studied about the activating area of the brain, when doing a cognitive task. The brain region ...
    Posted Apr 9, 2015, 6:07 PM by 서득원
  • [News] Autistic And Non-autistic Brain Differences Isolated Brain-Wide Association Analysis(BWAS) is the method developed by researchers at the University of Warwick. It shows panoramic views of the brain, and it can help researchers to study ...
    Posted Apr 2, 2015, 5:19 PM by 서득원
  • [NEWS] The neuroscience behind hot-headed emails By scanning people's brain when they make decisions, scientist discovered that stress or emotion involved in in brain can cause thinking pattern change. In calm state, frontal lobes of ...
    Posted Mar 28, 2015, 6:19 AM by 서득원
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