The significance of this investigation was the evaluation of the possibility of adopting EEG functional coupling for the purpose of LD. Current EEG-based LD methods are based primarily on single-electrode measurements. In contrast, most fMRI-based studies have focused only on the exploration of activated brain regions when a subject engages in a lie. The functional connectivity of neuroelectric activities in different brain regions during lying has not yet been thoroughly investigated. WC was utilized in this study as a new LD index to characterize EEG functional connectivity between different brain regions during deception. In particular, LFCNs based on different frequency bands were proposed in this study. To the best of our knowledge, this study is the first to generate a LFCN.

Function and cooperation of activated brain regions in the LFCN

It is necessary to point out that our functional connectivity analysis was based on brain-scalp regions instead of on brain-cortical regions. The experimental results in this study indicate that when lying occurs, the prefrontal, frontal, parietal and central scalp regions were synchronously activated, going with the neuroelectric activity, particularly in the θ band. Notably, this finding is largely consistent with most previous fMRI/EEG studies in terms of the examination of activated brain-cortical or brain-scalp regions.

Over the past two decades, an increasing number of studies have indicated that the prefrontal and parietal cortical or scalp regions play an important role in the process of lying1,2,9,11,12,24,31. To date, many investigators have found that the P300 wave is usually the largest at Pz (the middle parietal scalp region) and the smallest at Fz (the middle frontal scalp region), taking intermediate values at Cz (the middle central scalp region)5,12,29. Accordingly, many EEG-based LD studies have acquired P300 waves on one of the scalp regions listed above29, which strongly suggests the important roles of the frontal, parietal and central scalp regions in LD. Despite the diversity of previous studies, one of the most consistent findings is that, compared with honest responses, lying engenders greater activity within and hence activates the joint F region (including the corresponding cortical regions), which plays a crucial role in processing the lying. Based on the θ LFCN presented in this study, we also found that the joint F region has an important role in lying.

Furthermore, most existing studies have indicated that the parietal regions are related to the execution of deception32. Johnson-Frey et al.33 reported that the left parietal region is a critical node for the planning of skilled movement; this finding agrees with our current results showing that P3 had a stronger and more connected relationship with the whole frontal region than Pz or P4 (see Fig. 8B). Additionally, based on the θ LFCN, the two strongest connectivities among all connections were found at P3- F3 (Δ3 = 0.121) and P3- F4 (Δ3 = 0.113). It should be stressed that the present study specifically focused on comprehensively and deeply interpreting the cooperative relationship between the abovementioned activated brain regions when lying occurs because the connectivity strength in the LFCN represents the difference in the functional connectivity between the two groups of subjects as opposed to the strength of the functional connectivity in only the guilty group.

Most existing fMRI-based LD studies claim to have analyzed neural correlations32. However, previous studies have only correctly identified activated brain regions when lying occurs and thus can be considered to have analyzed functional segregation34. In contrast, with the analysis of functional connectivity, our investigation indicated that three brain scalp regions—the prefrontal/frontal, central and parietal regions—were synchronously activated with different connectivity strengths when lying occurred and that these regions worked through a cooperative pattern of neural activity that demonstrated the obvious feature of low frequency.

Time-frequency analysis of functional connectivity

Considering neuroimaging methodologies, fMRI, with a temporal resolution of approximately 1 s, is limited to modeling hemodynamic evoked responses. Poor temporal resolution makes it difficult to resolve dynamic changes in functional connectivity over a short period of time30. In contrast, EEG has high temporal resolution (<1 ms) and is therefore more optimal for the calculation of functional connectivity. In the present analysis, the synchronic time period for the neuroelectric activity in the proposed LFCN was limited to within only 350 ms, which is more accurate than the period analyzed by fMRI-based LD studies. This accuracy greatly contributes to an in-depth understanding of the dynamic processing that occurs during deception. The statistical results associated with the LFCNs proposed in this study demonstrate the advantages of our study over fMRI investigations.

The present study also has several advantages over most existing EEG-based LD studies. First, only a simple time or frequency analysis is used for most current LD methods. In contrast, time-frequency analysis can capture the overall features of non-stationary EEG signals. From this perspective, WC has merits over traditional time or frequency analysis due to the inherent advantages of wavelet analysis. Our experiment showed that the most significant difference between lying and telling the truth was found in the θ band during the 3# time period. Second, our study fills a gap between investigations based on a single electrode and studies involving multiple brain regions. In previous investigations, the measurements or features used to test lying were usually calculated and extracted from a single electrode29. In contrast, combining more electrodes or brain regions, such as in the present study, could make it easier to identify more valuable information, particularly when a certain task is accomplished via the cooperation of multiple brain sites. Accordingly, analysis of functional connectivity, such as correlation or coherence, can fully unearth key information from multiple brain sites. LD methods that use a single electrode are unable to accurately detect the wide distribution of neuroelectric activities that are present in lying responses18. The experimental results in the present study provide greater insight into the cooperative working patterns and neural connectivities that form between different brain regions during the production of lying responses.

LD study using a single trial

Most current LD studies have required a number of stimuli to generate a complete identification result for one subject4,5. For example, the bootstrapped amplitude difference and the bootstrapped correlation difference15,29,35 methods cannot give an identification result until all stimuli are presented to the subject. However, the method proposed in the present study is based on the level of single trials; therefore, it is highly flexible for both testers and subjects15,29 and can greatly decrease subject fatigue and the risk of countermeasures. Second, compared with conventional ERP analysis4,5 and event-related coherence studies36, single-trial analysis investigating the distribution of coherence values for repeated stimuli37 could provide deeper insight into the neural mechanisms that underlie deception. For example, one could catch the time course associated with changes in functional coupling and then evaluate changes in neural mechanisms in the brain across trials. Third, in the present study, we emphasized that discrimination between lying and truth-telling should be achieved at the level of single trials to translate a theoretical study into a practical application9,11,12,29 instead of at the level of group analysis, which is favorable for LD and might also allow the possibility and increase the feasibility of implementing a real-time system for LD.

Classification using the LFCN

It should be stressed that one of the most important purposes of LD is to distinguish guilty from innocent subjects. However, only a few studies using fMRI have proposed a classification method while simultaneously providing its sensitivity and specificity. In the present study, a machine learning-based classification method was presented to separate lies from truthful responses using WC-based features and a SVM. High classification accuracies, including high sensitivity and specificity, were obtained for the training and testing datasets, strongly supporting the view that it is reasonable and feasible to utilize the WC method in EEG to detect deceptive responses and hence to distinguish guilty from innocent subjects.

Limitations

Interactions between different brain regions can be analyzed by bidirectional and unidirectional coupling18. The former can be assessed by functional connectivity, whereas the latter reflects a causal interaction between the proactor (the initiating source) and the reactor (the driven object). Obviously, neither previous LD work nor our current investigation can solve the latter coupling question. Hence, unidirectional coupling should be further assessed in future studies, which should increase understanding of the neural dynamics that are associated with the lying process. Various methods, such as a directed transfer function38, can then be applied to investigate the causal interaction mentioned above18,39.

Moreover, source domain connectivity has more reliable physiological interpretations when using EEG to analyze neural functional connectivity, for which the so-called inverse problem must be solved and the volume conductor effect should be considered40. Two major methods could be applied in this regard. A major solution to this problem is to compute the functional coupling between equivalent intracranial current dipoles. An inverse problem is an ill-posed problem41, and the optimal solution depends on many constraints and assumptions regarding information about dipoles, such as their moments, positions, magnitudes and orientations42,43. Additionally, a number of possible model configurations that fit well with the spatial patterns of scalp EEG potentials are needed44. It is obvious that many of the above factors will affect the accuracy with which a source can be localized42,43,45,46. Furthermore, theoretically, only an infinite number of recording electrodes could obtain the unique location of each of the responsible sources47. The second method consists of computing the coupling between brain regions of interest48,49, which still involves considerable subjective procession and a priori knowledge regarding the locations of neural current generators underlying scalp EEG recordings44. In sum, the localized sources within the brain are based on some a priori assumptions and knowledge50, so the locations are non-unique, and the connectivity analysis is still unreliable42,45. In the present study, a common average reference was first used to assure that WCs were independent in the reference of the EEG recording30,38. Moreover, we recorded EEG signals from 12 electrodes instead of 64 or more channels to increase the distance between the neighboring channels to the greatest degree possible, which could alleviate the negative effect of the volume conduction to some extent38,51. Kaminski and Blinowska noted that the spread of electrical activity becomes smeared by volume conduction, and they obtained clear and reproducible results in their study51. Similarly to many recent reports using EEG to assess brain functional connectivity18,30,38,45,52,53,54,55,56, we also assessed functional connectivity patterns in scalp regions instead of in the source domain. We emphasize that both the experimental results and the analyses in the present study were restricted to brain-scalp regions. Even so, as discussed previously, the brain-scalp regions that were identified as becoming activated during lying were largely consistent with the activated cortical regions identified in most previous fMRI studies. This concordance demonstrates that the results in the present study were hardly affected by volume conduction and are therefore credible. Evaluating the functional connectivity patterns that form during lying in the source domain is beyond the scope of this study, although we may investigate these patterns in future studies.

Finally, notwithstanding the good temporal resolution of EEG, our proposed approach also has the shortcoming of poor spatial precision. A growing number of studies have come to recognize the great advantages of employing both neuroimaging and neurophysiological methodologies. For example, a study by Sun et al.23 suggested that combining EEG and fMRI could provide insights into both the spatial and temporal features that are associated with the neural process of deception. Hence, enhancing the spatiotemporal accuracy of investigations into the neural process of lying is a future research goal that could be achieved using the multimodal fusion approaches mentioned above.