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.

Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG

Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human-computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), Rényi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature.

Magnetic Resonance Imaging Assessment of Fatigue Injury during Exercise

In order to investigate the changes of temporal function metabolism of lumbar and back muscles after exercise, a magnetic resonance imaging- (MRI-) based assessment of fatigue injury during exercise was proposed. A total of 100 healthy adult volunteers were selected, including 48 males and 52 females, aged from 19 to 30 years, with an average age of 24.8 + 2.3 years. They were divided into four groups according to the different time points of 0, 15, 30, and 45 minutes after exercise, with 25 persons in each group. PHILIPS Achieva 3.0 T Tx MRI scanner was used to perform BOLD and T2-mapping before and after exercise in four groups of healthy volunteers. All data were analyzed by statistical software. The results showed that the total CSA of the dorsi extensor muscle group and the CSA value of the dorsi extensor muscle group at different levels at different time points before and after exercise increased slowly in exercise period and decreased rapidly in recovery period. It was verified that BOLD MRI and T2-mapping imaging can indirectly evaluate the trend of CSA water metabolism and blood oxygen level in healthy adults after exercise.

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

The countermeasure of driver fatigue is valuable for reducing the risk of accidents caused by vigilance failure during prolonged driving. Listening to the radio (RADIO) has been proven to be a relatively effective “in-car” countermeasure. However, the connectivity analysis, which can be used to investigate its alerting effect, is subject to the issue of signal mixing. In this study, we propose a novel framework based on clustering and entropy to improve the performance of the connectivity analysis to reveal the effect of RADIO to maintain driver alertness. Regardless of reducing signal mixing, we introduce clustering algorithm to classify the functional connections with their nodes into different categories to mine the effective information of the alerting effect. Differential entropy (DE) is employed to measure the information content in different brain regions after clustering. Compared with the Louvain-based community detection method, the proposed method shows more superior ability to present RADIO effectin confused functional connection matrices. Our experimental results reveal that the active connection clusters distinguished by the proposed method gradually move from frontal region to parieto-occipital regionwith the progress of fatigue, consistent with the alpha energy changes in these two brain areas. The active class of the clusters in parieto-occipital region significantly decreases and the most active clusters remain in the frontal region when RADIO is taken. The estimation results of DE confirm the significant change (p < 0.05) of information content due to the cluster movements. Hence, preventing the movement of the active clusters from frontal region to parieto-occipital region may correlate with maintaining driver alertness. The revelation of alerting effect is helpful for the targeted upgrade of fatigue countermeasures.