The human brain is composed by biological neural network, which is a series of interconnected neurons whose activation defines a recognizable linear pathway. The interface through which neurons interact with their neighbors usually consists of several axon terminals connected via synapses to dendrites on other neurons. Through the neural network, we could predict how the external stimulation might affect the functions of the brain and which pathway might be related. Furthermore, it might help to understand the underlying mechanisms of functional activities of the brain.
Sleep Mechanism Modeling
As an extension of the sleep monitoring research, we will introduce the model for explaining the sleep mechanism. Information flow, the location of signal sources and sinks are could be figured out through the signal processing methods. We can merge the well-known neuroscientific interpretation of sleep mechanisms with this structural and intuitive information. In this way, we can find the ideal brain models for the normal person and insomniac. Our laboratory expects that this research will contribute to a better explanation about somnipathy.
Alzheimer's Disease Progress Modeling
There are common indicators (for example, Tau protein and Amyloid-beta) that are using for Alzheimer’s disease diagnosis which comes from the biological examination. The examination can be fulfilled by cerebrospinal fluid (CSF) examination or medical imaging techniques (MRI or PET). CSF examination is painful because it needs lumbar puncture and examination using medical imaging devices is expensive. We are looking for finding some features in Alzheimer’s EEG signal which are related to symptoms of the disease. EEG measurement is a non-invasive method that is very comfortable and cheap compared to CSF examination and MRI. Furthermore, using the EEG features related to Alzheimer’s disease symptoms, the progress of disease could be traced. Our final goal is brain modeling which reflects Alzheimer’s disease progression.