Resampling the peak, some dos and don'ts

Resampling techniques are used widely within the ERP community to assess statistical significance and especially in the deception detection literature. Here, we argue that because of statistical bias, bootstrap should not be used in combination with methods like peak-to-peak. Instead, permutation tests provide a more appropriate alternative

A new method for detecting deception in Event Related Potentials using individual-specific weight templates [Abstract]

A new method called the weight template (WT) is proposed for classifying Event related potentials (ERPs) into deceiving and non-deceiving. In this study, EEG data from two P300-based lie detection experiments were analyzed to demonstrate the efficiency of the WT method in detecting deception. A comparison was made with a common method used to measure P300 presence, called Peak-to-Peak, which is believed to be more accurate than other methods in measuring P300 amplitudes [1, 2]. One experiment consisted of presenting participants with birth date stimuli and 12 participants were instructed to lie about their own birthday. The other experiment consisted of 15 participants who were instructed to lie about their first names [3]. Using simulated EEG data [4], Receiver Operating Characteristic (ROC) curves were also generated to examine the efficiency of the proposed method in detecting deception in low signal-to-noise ERPs. Typically, P300-based lie detection systems employ the P300 component to detect concealed information. They present three stimulus types: Probes (P), which represent concealed information or crime details and can be recognized only by the guilty person; Irrelevants (I), which are frequent and task (crime)-irrelevant, and Targets (T), which are irrelevant items, but participants are asked to do a task whenever they see a Target. For practical lie detection, the key comparison is between Probe and Irrelevant ERPs, since, for the nondeceiver, the former would be an Irrelevant. Importantly, the Probe for a deceiver typically generates a P300 ERP component, which is absent for the Irrelevant. The principle underlying the WT method is that as the Target stimulus is task-relevant, it will evoke a robust P300 pattern for each subject, which we hypothesize is characteristic in form and polarity of that individual's P300. Accordingly, this TERP can serve as an individual-specific template, with which to search for the Probe P300. Specifically, the difference between Tand IERPs was used as a template (i.e. effectively as a kernel) and this template was applied to Pand IERPs. Using such a template, with some pre-processing steps, we found that the WT achieved significantly better detection performance in comparison to Peak-to-Peak. In the names lie detection, the WT was able to detect deception for 93% in the guilty group compared with 80% by Peak-to peak. The false alarm rates using WT and Peak-to-Peak were 2% and 8% respectively. In the birthdays lie detection, hit rates were 50% using WT and 33% using Peak-to-Peak. The false alarm rates of both methods were 5%. ROC curve analysis also showed that in ERPs with high signal-to-noise ratio (SNR), both methods could detect deception successfully and almost equally. However, the WT performed better in ERPs with low SNR. We thus conclude that the WT is simple and very effective for detecting deception, even in ERPs with low SNR

Does mixed reality have a Cassandra Complex?

Recent years have seen a boom in Virtual, Augmented, and Mixed Reality technologies which have been widely adopted both by the consumer market and the research community. These technologies have provided researchers the ability to generate and gather data in new ways, through world building and scenario creation in every environment imagined. Although this growing interest is exciting, there is also a mounting concern about best practises and ethical dilemmas. In the literature one can already find a large quantity of papers providing guidelines and raising ethical concerns. However, ethical pitfalls continue to be overlooked. In this opinion paper, prompted by the ethics developments in Artificial Intelligence (AI), another area with rapid growth and adoption which has been overwhelmed by a huge number of guidelines and is still nowhere close to universal acceptance of standards, we propose that the virtual, augmented, and mixed reality research and development areas need to come together as whole; involving government, industry and science in order to define, develop and decide guidelines and strategies before we replicate the devastating consequences such as decaying trust in technology witnessed in other areas like social media

Breakthrough percepts of famous names

Studies have shown that presenting own-name stimuli on the fringe of awareness in Rapid Serial Visual Presentation (RSVP) generates a P3 component and provides an accurate and countermeasure resistant method for detecting identity deception (Bowman et al., 2013, 2014). The current study investigates how effective this Fringe-P3 method is at detecting recognition of familiar name stimuli with lower salience (i.e., famous names) than own name stimuli, as well as its accuracy with multi-item stimuli (i.e., first and second name pairs presented sequentially). The results demonstrated a highly significant ERP difference between famous and non-famous names at the group level and a detectable P3 for famous names for 86% of participants at the individual level. This demonstrates that the Fringe-P3 method can be used for detecting name stimuli other than own-names and for multi-item stimuli, thus further supporting the method's potential usefulness in forensic applications such as in detecting recognition of accomplices. (c) 2021 Elsevier Ltd. All rights reserved.Peer reviewe

Breaking the Circularity in Circular Analyses: Simulations and Formal Treatment of the Flattened Average Approach

There has been considerable debate and concern as to whether there is a replication crisis in the scientific literature. A likely cause of poor replication is the multiple comparisons problem. An important way in which this problem can manifest in the M/EEG context is through post hoc tailoring of analysis windows (a.k.a. regions-of-interest, ROIs) to landmarks in the collected data. Post hoc tailoring of ROIs is used because it allows researchers to adapt to inter-experiment variability and discover novel differences that fall outside of windows defined by prior precedent, thereby reducing Type II errors. However, this approach can dramatically inflate Type I error rates. One way to avoid this problem is to tailor windows according to a contrast that is orthogonal (strictly parametrically orthogonal) to the contrast being tested. A key approach of this kind is to identify windows on a fully flattened average. On the basis of simulations, this approach has been argued to be safe for post hoc tailoring of analysis windows under many conditions. Here, we present further simulations and mathematical proofs to show exactly why the Fully Flattened Average approach is unbiased, providing a formal grounding to the approach, clarifying the limits of its applicability and resolving published misconceptions about the method. We also provide a statistical power analysis, which shows that, in specific contexts, the fully flattened average approach provides higher statistical power than Fieldtrip cluster inference. This suggests that the Fully Flattened Average approach will enable researchers to identify more effects from their data without incurring an inflation of the false positive rate

Breakthrough Percepts of Famous Faces

Recently, we showed that presenting salient names (i.e. a participant’s first name) on the fringe of awareness (in Rapid Serial Visual Presentation) breaks through into awareness, resulting in the generation of a P3, which (if concealed information is presented) could be used to differentiate between deceivers and non-deceivers (Bowman et al., 2013; Bowman, Filetti, Alsufyani, Janssen, & Su, 2014). The aim of the present study was to explore whether face stimuli can be used in an ERP-based RSVP paradigm to infer recognition of broadly familiar faces. To do this, we explored whether famous faces differentially break into awareness when presented in RSVP and, importantly, whether ERPs can be used to detect these ‘breakthrough’ events on an individual basis. Our findings provide evidence that famous faces are differentially perceived and processed by participants’ brains as compared to novel (or unfamiliar) faces. EEG data revealed large differences in brain responses between these conditions

Detecting Perceptual Breakthrough in RSVP with Applications in Deception Detection Methodological, Behavioural and Electrophysiological Explorations

This thesis explores perceptual breakthrough in rapid serial visual presentation (RSVP), for deception detection applications. In RSVP, visual stimuli are presented in rapid succession, pushing the perceptual processing system to the limit, allowing only a limited number of stimuli to be processed and en- coded. In this thesis we investigate what type of stimuli capture attention in RSVP, taking advantage of both physiological and behavioural measurements. The main focus of the studies presented here follows up on work that shows that perceptual breakthrough in RSVP can be used as a marker of concealed knowledge in deception detection tests (Fringe P300). The thesis is divided into two research contribution parts. Firstly, we develop methods for analysing Event Related Potential (ERP) data, in order to facilitate assessment of perceptual breakthrough in experiments presented later in this thesis. We focus on reducing false positives while at the same time successfully measuring the underlying effects. We present and evaluate methods for measuring latencies and selecting Regions of Interest (ROIs) through simulations and experimental data. Secondly, we explore perceptual breakthrough in RSVP with applications in deception detection. For that purpose, we conducted two studies. The first study explores incidentally acquired information by recording the P300 ERP component from participants after acting out a mock crime scenario. The main hypothesis was that concealed information is salient to a guilty person, and thus associated stimuli will be involuntary perceived. The second study explores the type of stimuli that capture attention in RSVP, by addressing issues related to encoding and emotional arousal, and whether attention can be directed through contextual priming independent of the main task. These studies increase our understanding of how stimuli are processed in RSVP and can provide useful suggestions for designing more successful ERP and RSVP based, deception detection applications, both in terms of stimulus presentation and data analysis