Multiple Correlated Component Analysis for Identifying the Bilateral Location of Target in Visual Search Tasks

Abstract

N2pc is defined as a negative event-related potential component that appears after about 250 ms at posterior electrodes contralateral to a target’s location in visual search, which can be used to measure attentional shifts between bilateral visual hemifields and locate the spatial location of lateral targets. However, the waves between the left and right hemispheres elicited by lateral targets usually exhibit a small amplitude difference and strong synchronicity, which may lead to low classification performance. Therefore, the present study explored the feasibility of a multiple correlated components analysis (MCORCA) methods to identify the lateral targets in visual search tasks with a single trial, which could weight the target signals by spatial filters to enlarge the amplitude difference between bilateral hemispheres and extract the linear combinations of multiple channels across trials with an optimal subset of correlated components to avoid the loss of efficient information. The classification rate achieved 82% with a single short-duration trial when using the proposed method with Leave-one-out-cross-validation (LOOCV). The findings demonstrated that the MCORCA-based methods could be used to improve the classification performance for the N2pc-based brain-computer interfaces (BCI) in visual search.