Signal translation between EEG and ECoG to improve non-invasive based BCI performance

An electroencephalography (EEG) / electrocorticography (ECoG) inverse model for the Brain-Computer Interface (BCI) was developed, and the analysis of the signals was simulated in Python environment. The inverse solution, in an attempt to estimate ECoG from EEG, can significantly improve the performance of noninvasive based BCI. NonLinear Principal Component Analysis (NLPCA) is employed to reduce the complexity of computation. Forward model is then derived from the electro-physiological perspective to capture the dynamic of the signals. To represent nonlinear approximations, a NeuroBondGraph (NBG) approach is introduced to model both the system dynamics and the nonlinearity in a more efficient way. Inverse solution is then established integrating with the de-mapping part of NLPCA. The simulation results are demonstrated by the comparison between original signals and reconstructed signals from our model.