Keiichiro Nishida

The paper identifies biases in brain connectivity inference from electrophysiological signals due to noise and introduces a method to correct for these biases.

The paper introduces a method to measure Granger-causal effects in multivariate time series by comparing system dynamics before and after node deletion.

This paper compares bivariate frequency domain measures of electrophysiological connectivity, showing LagCoh and sImCov outperform other methods under mixing and non-mixing conditions.

The paper compares EEG/MEG methods for localization error, false positive activity, and false positive connectivity, finding that sLORETA and eLORETA have exact localization while others perform worse.

This paper introduces new coupling measures for analyzing cortical brain signals by generalizing weighted multiple regression models and defining inhibitory coupling to capture inverse relationships in neural activity.

The paper introduces a new method for unmixing EEG/MEG inverse solution signals to correct for false connectivity, showing it produces accurate human connectomes under broad conditions.