Brain Science and Engineering using Neuroimaging Modality
Investigating brain functions measured by various neuroimaging modalities such as functional MRI (fMRI) and electroencephalography (EEG) toward potential applications such as health care systems including brain-computer interface (BCI) and brain-machine interface (BMI) to enhance human performance.
Now, the Question is..
Brain engineering and healthcare applications as well as enhancement of human performance via neuroimaging modalities are possible?
Aims
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 novel analytical methods such as independent vector analysis (IVA), iterative dual-regression of group independent component analysis (ICA) with a sparse prior to better estimate true neuronal activity, recursive principal component analysis (PCA) to EEG-segments of simultaneous EEG-fMRI data, and deep neural network (DNN) to fMRI data. The developed methods would gainfully be applied to the neuroimaging data including fMRI, simultaneous EEG-fMRI, 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.
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