Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

Abstract

A classification concealed information test (CIT) used the “brain fingerprinting” method of applying P300 event-related potential (ERP) in detecting information that is (1) acquired in real life and (2) unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to three types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified). We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational affiliation.

Electrophysiological markers of poor versus superior math abilities in healthy individuals

“…Having poor math skills led to larger P300s to incorrect solutions, as demonstrated by significant Spearman correlations. Since in match/mismatch paradigms P3/N400 potentials reflect correct/congruent versus incorrect/incongruent categorizations (e.g., Farwell et al., ; Kałamała et al., ; Proverbio & Zani, ), it might be argued that low math skilled individuals had reduced abilities to reject incorrect solutions. This hypothesis is supported by Spearman correlations showing an inverse relationship between performance and P300 amplitude to incorrect solutions.…”

“…On the basis of previous literature, we hypothesized that arithmetic incongruities (i.e., math operations paired to an incorrect result) would considerably reduce the amplitude of P300 responses, whose amplitude would be increased by correct solutions. P300 component (or P3b) is one of the most studied brain potentials linked to categorization and decisional processes (e.g., Johnson, ; Kirschner, Cruse, Chennu, Owen, & Hampshire, ; Polich, ) in addition to other functions, and it is always larger to congruent than incongruent pairs (e.g., Bach, Gunter, Knoblich, Prinz, & Friederici, ; Farwell, Richardson, Richardson, & Furedy, ; Kałamała, Szewczyk, Senderecka, & Wodniecka, ; Proverbio, Calbi, Manfredi, & Zani, ). It also reflects response certainty and subjective confidence in making a decision (e.g., Dröge, Fleischer, Schlesewsky, & Bornkessel‐Schlesewsky, ).…”

Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls

“…A concrete example of an application that fulfills the criteria of no or unreliable alternative measures of the mental state of interest and reliable neurophysiological signals, is “concealed information detection” (Farwell et al, 2014 ). In this work ERPs are used to determine whether or not it is likely that a person under “criminal” investigation possesses certain information.…”

Editorial: Using neurophysiological signals that reflect cognitive or affective state

“…Many studies follow a so-called passive Brain-Computer Interface approach (Zander and Kothe, 2011 ): assessing typically covert aspects of the user state without interfering with the task the user is doing. Applications addressed in this volume include improved learning and training environments (Gerjets et al, 2014 ; Stikic et al, 2014 ), adaptive vehicle interfaces (Dijksterhuis et al, 2013 ; Touryan et al, 2014 ; Wang et al, 2014 ), classifying concealed information (Farwell et al, 2014 ), identifying sleep stages (Huang et al, 2014 ), and non-verbal communication for patients (Kashihara, 2014 ). Making these applications viable is not trivial.…”

“…More particular, information derived from a single electrode location (Pz) turns out to be an adequate workload predictor. Finally, papers also demonstrate the use of a wide variety of indices from a single source, for instance EEG is analyzed both in the time domain (P3 event related potentials, Farwell et al, 2014 and error-related potentials, Chavarriaga et al, 2014 ) and the frequency domain (as workload index, Mühl et al, 2014 or in both, Hogervorst et al, 2014 ). Finally, Gerjets et al ( 2014 ) and Zander and Jatzev ( 2012 ) argue that contextual information can be a useful addition to neurophysiological measures.…”