We discriminated target vs. standard trials by applying a linear classifier to the 43-channel EEG signal amplitude, xτ(t), using the sliding window method of Parra et al. (2002; 2005). This method is described comprehensively inGoldman et al. (2009) andSajda et al. (2010), and an overview is illustrated in Figure 1. We selected a training window of width 50 ms and varied the window center, τ, across the entire epoch (stimulus-locked 0–1000 ms, response-locked −600–400 ms) in overlapping 25 ms increments. For each of these time windows, we used logistic regression on the 50 ms of data to estimate the linear weighting, wτ, of the EEG sensors resulting in a projection, yτ(t), that maximally discriminated the conditions. (1)yτ(t)=wτTxτ(t) The classifier output is the distance of each trial from the discriminating hyperplane, representing the classifier’s confidence in its prediction based on the training data and the model. We treat this as a surrogate for the neural confidence of the decision, which for our simple target-detection task is primarily dependent upon the subjects’ instantaneous task-engagement.
