An integrated Gaussian mixture model to estimate vigilance level based on EEG recordings

Past, Present, and Future of EEG-Based BCI Applications

An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

Prediction of Response Time and Vigilance Score in a Sustained Attention Task from Pre-trial Phase Synchrony using Deep Neural Networks

A real-time assessment of sustained attention requires a continuous performance measure ideally obtained objectively and without disrupting the ongoing behavioral patterns. In this work, we investigate whether the phasic functional connectivity patterns from short- and long-range attention networks can predict the tonic performance in a long Sustained Attention to Response Task (SART). Pre-trial phase synchrony indices (PSIs) from individual experiment blocks are used as features for assessment of the proposed average cumulative vigilance score (CVS) and hit response time (HRT). Deep neural networks (DNNs) with the mean-squared-error (MSE) loss function outperformed the ones with mean-absolute-error (MAE) in 4-fold cross-validations. PSI features from the 16-20 Hz beta sub-band obtained the lowest RMSE of 0.043 and highest correlation of 0.806 for predicting the average CVS, and the alpha oscillation PSIs resulted in an RMSE of 51.91 ms and a correlation of 0.903 for predicting the mean HRT. The proposed system can be used for monitoring performance of users susceptible to hypo- or hyper-vigilance and the subsequent system adaptation without implemented eye trackers. To the best of our knowledge, functional connectivity features in general and phase locking values in particular have not been used for regression models of vigilance variations with neural networks.