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