The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.

To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.

Epilepsy is a severe neuropsychiatric disorder, the cause of which remains to be elucidated. Genome‑wide association studies, DNA microarrays and proteomes have been widely applied to identify the candidate genes involved in epileptogenesis, and integrative analyses are often capable of extracting more detailed biological information from the data. In the present study, a total number of 1,065 genes in different animal models were collected to construct an epilepsy candidate gene database. Further analyses suggested that the response to organic substances, the intracellular signaling cascade and neurological system processes were significantly enriched biological processes, and the mitogen-activated protein kinase pathway was identified as a putative epileptogenic signaling pathway. In addition, the five key genes, growth factor receptor bound 2, amyloid β (A4) precursor protein, transforming growth factor‑β, vascular endothelial growth factor and cyclin‑dependent kinase inhibitor 1, were identified as being critical as central nodes in the protein networks. Reverse transcription‑quantitative polymerase chain reaction analysis revealed that these genes were all upregulated at the mRNA level in the epileptic loci compared with the resection margin of tissue samples from the same patients diagnosed with epilepsy. The data mining performed in the present study thus was shown to be a useful tool, which may contribute to obtaining further information on epileptic disorders and delineating the molecular mechanism of the associated genes.