Event-related Potential analysis using elastic net logistic regression

Abstract

The objective of the thesis is to explore whether regularization techniques can be applied to ERP analysis, and which type of regularization is adequate. This thesis proposes elastic net regularization logistic regression as a good candidate of data analytic method for Event-Related Potential analysis (ERP). Specifically, regularization techniques are used to identify latency in ERP. Study 1 tested whether regularization logistic regression can classify latency using simulated ERP data. It showed that ridge and lasso could identify latency information. In study 2, the same analyses were applied to actual ERP data. Ridge regression can identify latency information wheras lasso cannot.