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Cross Frequency Coupling (CFC)

 

 

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Cross Frequency Coupling (CFC) is the interaction between brain oscillations of different frequencies, and the coupling phenomenon has been observed in the brain of rodent and human. Phase-amplitude coupling (PAC) is a type of CFC, which describes the dependence between the phase of a low-frequency component and the amplitude of a high-frequency component of electrical brain activities. It has been claimed that the modulation of low frequency phase on high frequency amplitude plays a functional role in cognition and information processing, such as learning and memory. The change of PAC patterns has been associated with various neurological disorders, e.g., epilepsy and  schizophrenia.

Five seizure stages classified by firing patterns (on the left). PAC pattern comparisons between the conventional method (middle) and HHT method (right) of two patients.
Five seizure stages classified by firing patterns (on the left). PAC pattern comparisons between the conventional method (middle) and HHT method (right) of two patients.
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Non-stationary and nonlinear techniques for seizure detection algorithms

Epilepsy is one of the most common neurological diseases, affecting over 3 million people in U.S. and 50 million (~1%) people worldwide. An automated and accurate seizure detection method can be very helpful. Currently, most people use stationary and linear methods (i.e. Fast Fourier Transform (FFT), etc.) to analyze the signal. However, EEG signal is non-stationary and nonlinear in nature, thus these methods will introduce inaccuracy. Hence, we are developing non-stationary and nonlinear algorithms to improve the accuracy of seizure detection.
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Collaborators

  1. Dr. Yue-Loong Hsin, Chung Shan Medical University, Taiwan
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Electrode-based Bio-imaging

Our goal is to develop an Electrode-based Bio-imaging System that will be fMRI-competitive. Currently, fMRI is very popular since it offers high spatial resolution (~3mm); however, its temporal resolution is limited (~1s) and is impossible to be used in portable applications. EEG has excellent temporal resolution (~1ms), portable mobility that allowed to be used in daily life. Here, we devote to improving the spatial resolution of EEG in the aspects of both hardware and software.
 
1. Hardware
We developed a Focused Electrode that improves spatial resolution, recording SNR and reduce crosstalk and correlation with adjacent electrodes. The Focused Electrode is adaptive to the geometry parameters of electrode array including electrode size, pitch and source depth, such as EEG or ECoG. Simulation results show that the Focused Electrode increases the number of electrodes up to 7x in EEG and 30x in ECoG without overlapping information if the array covers half of head.

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2. Software
We study the inverse imaging using realistic head model based on MR image (NFT toolbox). On one hand, we are studying the influence of various factors (i.e. noise, electrode number, head model, inverse solution, etc.) on inverse imaging; on the other hand, we are developing more accurate inverse imaging algorithms.

 

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Collaborators

  1. Prof. Scott Makeig, SCCN, UCSD
  2. Dr. Yue-Loong Hsin, Chung Shan Medical University, Taiwan
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Brain Dynamics and Source Localization

Brain imaging techniques can help us explore the function of human brain (i.e. memory, behavior, etc.) and help diagnosis and treatment of brain disorders (i.e. seizure, depression, etc.). Imaging tool with high temporal and spatial resolution is highly desirable. Currently, fMRI is very popular since it offers high spatial resolution (~3mm); however, its temporal resolution is limited (~1s) and is impossible to be used in portable applications. EEG has excellent temporal resolution (~1ms), portable mobility that allowed to be used in daily life. Our goal here is to improve the spatial resolution of EEG to be as good as that of fMRI.

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We study the brain dynamics and source localization based on realistic head model (NFT toolbox). On one hand, we are studying the influence of various factors (i.e. noise, electrode number, head model, inverse solution, etc.) on the accuracy of inverse imaging; on the other hand, we are developing more accurate inverse imaging algorithms.

 

 

 

Collaborators

  1. Prof. Scott Makeig, SCCN, UCSD
  2. Dr. Yue-Loong Hsin, Chung Shan Medical University, Taiwan