Comparison of different functional connectives based on EEG during concealed information test

EEG-based multi-band functional connectivity using corrected amplitude envelope correlation for identifying unfavorable driving states

Recognition of unfavorable driving state (UDS) based on Electroencephalography (EEG) signals and functional connectivity has a significant contribution to reducing casualties. However, when the functional connectivity approach directly applies to recognize drivers' UDS, it may encounter great challenges, because of spurious synchronization phenomenon. We introduce a novel functional connectivity matrix construction approach combined with the ensemble algorithm to identify drivers' UDS in the research. First, EEG data from a previously designed simulated driving experiment containing two driving tasks are extracted, and then functional connectivity matrix construction approach based on amplitude envelope correlation with leakage correction (AEC-c) in multiple frequency bands are calculated. Furthermore, the random subspace is utilized to improve the performances of the k-nearest neighbors (KNN) algorithm. Classification performances of the proposed approach are assessed by confusion matrix, accuracy (ACC), sensitivity (SEN), specificity (SPF), precision (PRE) and receiver operating characteristic (ROC) curve with 5-fold cross-validation strategy. The statistical analysis shows that the regional AEC-c values of 30 EEG channels for the driver's UDS are overall significantly lower than those for the driver's non-unfavorable driving state (NUDS) in the beta, gamma and all frequency bands. Further analysis about performance results shows that the proposed AEC-c-based functional connection matrix analysis approach in all frequency bands combined with the random subspace ensembles KNN achieves a highest ACC of 96.88%. The results suggests that our proposed framework is beneficial for EEG-based driver's UDS recognition, which is helpful to the transmission and interaction of information in man-machine system.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.