Bootstrapping the P300 ERP
In the field of deception detection, we are expected to classify an individual as guilty/knowledgeable or innocent/unknowledgeable, and hence analyze the data within-subjects (specifically, within anindividual ). This analysis would use single trial EEG data, which is often noisy and variable, leading to unreliable classification decisions. Therefore, in these situations it is especially useful to use simulation techniques like bootstrapping to aid intraindividual diagnosis (Wasserman & Bockenholt, 1989). The bootstrapping process involves selecting a set of single sweep ERP data points (with replacement) and averaging them to produce a single ERP amplitude per individual. Since the resampling of data points is done with replacement, the averages produced in each run will be slightly different from other runs. Within a subject, we can bootstrap with replacement a sample of probe trials and calculate the average, and similarly produce an average using irrelevant trials. Then, we compare the difference between resampled probes and irrelevant averages to estimate the CIT effect. In iterating this process n times, one can estimate out of all iterations how many times the probe P300 average exceeded the irrelevant P300 average, enabling investigators to create a diagnostic index for classifying the subject as guilty/knowledgeable or innocent/unknowledgeable. This diagnostic metric is known as the bootstrap iteration score (BSITER) (e.g., Rosenfeld et al., 2015; Rosenfeld & Donchin, 2015), and has been published in psychophysiological experiments for several decades.