Prediction of drowsiness using EEG signals in young Indonesian drivers

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 Empirical Study on the Effect of Blended Scents in Driving Environments From a Neuro-Cognitive Perspective

BACKGROUND

An effective method that is easy to implement and widely applicable for improving driving performance and reducing driving risks remains a challenge. Although fragrances are widely used in daily driving, there is a gap between empirical research on everyday blended fragrances and functional fragrances clinical reports. In this study, a deliberately chosen blend of scent without overtly stimulating or functional proven evidence was tested for its potential to enhance performance in a driving environment.

METHOD

Thirty qualified young drivers were recruited to participate in the experiment. They were asked to watch a 15-min first-person perspective driving video to simulate a driving environment and then complete questionnaires and three sets of behavioral experiments while their brain activity was monitored by EEG.

RESULT

Participants in the scented environment exhibited statistically significant advantages in two cognitive tasks during behavioral measures. These findings were effectively supported by the EEG data, showing that beta waves exhibited more activity in the occipital and prefrontal cortex, enhanced theta waves were observed in the prefrontal cortex, and the TAB index characterizing driving fatigue was suppressed in the prefrontal cortex.

CONCLUSION

This empirical evidence highlights the potential of pleasant, natural, and blended scents in enhancing driving performance, suggesting that promoting the aromatherapy while driving as an easily applicable approach in daily life seems justified and expands the application of aromatherapy in daily life.

Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems.