Abdulmajeed Alsufyani

Subliminal Salience Search Illustrated: EEG Identity and Deception Detection on the Fringe of Awareness

We propose a novel deception detection system based on Rapid Serial Visual Presentation (RSVP). One motivation for the new method is to present stimuli on the fringe of awareness, such that it is more difficult for deceivers to confound the deception test using countermeasures. The proposed system is able to detect identity deception (by using the first names of participants) with a 100% hit rate (at an alpha level of 0.05). To achieve this, we extended the classic Event-Related Potential (ERP) techniques (such as peak-to-peak) by applying Randomisation, a form of Monte Carlo resampling, which we used to detect deception at an individual level. In order to make the deployment of the system simple and rapid, we utilised data from three electrodes only: Fz, Cz and Pz. We then combined data from the three electrodes using Fishers method so that each participant was assigned a single p-value, which represents the combined probability that a specific participant was being deceptive. We also present subliminal salience search as a general method to determine what participants find salient by detecting breakthrough into conscious awareness using EEG.

The cost of space independence in P300-BCI spellers

BackgroundThough non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user.MethodsIn healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller.ResultsEEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller’s performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed.ConclusionsThe advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus.

Countering Countermeasures: Detecting Identity Lies by Detecting Conscious Breakthrough

One major drawback of deception detection is its vulnerability to countermeasures, whereby participants wilfully modulate their physiological or neurophysiological response to critical guilt-determining stimuli. One reason for this vulnerability is that stimuli are usually presented slowly. This allows enough time to consciously apply countermeasures, once the role of stimuli is determined. However, by increasing presentation speed, stimuli can be placed on the fringe of awareness, rendering it hard to perceive those that have not been previously identified, hindering the possibility to employ countermeasures. We tested an identity deception detector by presenting first names in Rapid Serial Visual Presentation and instructing participants to lie about their own identity. We also instructed participants to apply a series of countermeasures. The method proved resilient, remaining effective at detecting deception under all countermeasures.

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

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 individuals 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.

ERP latency contrasts using Dynamic Time Warping algorithm

Latency contrasts are central to Event Related Potential (ERP) research. For example, they are used to determine the order and length of cognitive processes or to evaluate how experimental conditions influence processing time. The most popular methods employed by researchers are peak latency, fractional peak and fractional area. However there are difficulties with these methods, which often include acute sensitivity to noise and window placements [1]. In addition, they require parameter settings that are often difficult to justify. In order to address these issues, we propose Dynamic Time Warping (DTW), an algorithm used for measuring similarity between two sequences, and more precisely the use of the warping path and its relationship to the main diagonal as a measure of latency difference. We tested the performance of DTW by comparing it to 25% - 50% fractional area, peak and 50% fractional peak. In addition to the default DTW we also tested the type IIa step pattern, which constraints the resulting warping path [2]. Data were obtained from a deception detection experiment [3] and ERPs were generated from two channels for one condition. Then the second condition was created in two ways: firstly by offsetting the first condition by 100 time points (0.05 ms), and secondly by offsetting the first condition by an amount sampled from a normal distribution with a mean of 100 time points, while also varying the amplitude. In this way, we simulated latency jitter and amplitude variability between conditions as found in real ERP experiments. Each method was applied to windows placed accordingly to relevant experiments. This was done for each one of the channels (P3a at Fz and P3b at Pz). Then noise was added at the power spectrum of human EEG [4], and the performance of each method was evaluated through permutation tests (100 p-values) for different Signal-to-Noise Ratios (SNR). In order to test the methods independently of response bias, we performed ROC (Receiver Operating Characteristic) analysis, where the false positive rate was obtained by generating the second condition without any latency difference. We also compared DTWs sensitivity to window placement against 25% and 50% fractional area, by selecting a fixed time point as the start of the bounding window and then sequentially adjusting the end of the window and calculating the proportion of windows, for which each of the methods failed to determine the correct latency difference. Our analysis shows that the basic DTW performs at the same level as the fractional area method, which outperforms peak and fractional peak. The typeIIa DTW performs better than all the other methods for most SNRs. Although there is a slight inflation of false positives for high SNRs, the ROC analysis shows that it does not substantially affect DTWs performance. At the same time, DTW shows significantly less sensitivity to window placement than fractional area. These results indicate that DTW is a promising technique for determining latency differences, being more robust to noise and window placement, without being subject to the same number of assumptions or parameterisation as the other methods evaluated.