EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection

Brain activities can be evaluated by using Electroencephalogram (EEG) signals. One of the primary reasons for traffic accidents is driver fatigue, which can be identified by using EEG signals. This work aims to achieve a highly accurate and straightforward process to detect driving fatigue by using EEG signals. Two main problems, which are feature generation and feature selection, are defined to achieve this aim. This work solves these problems by using two different approaches. Deep networks are efficient feature generators and extract features in low, medium, and high levels. These features can be generated by using multileveled or multilayered feature extraction. Therefore, we proposed a multileveled feature generator that uses a one-dimensional binary pattern (BP) and statistical features together, and levels are created using a one-dimensional discrete wavelet transform (1D-DWT). A five-level fused feature extractor is presented by using BP, statistical features of 1D-DWT together. Moreover, a 2-layered feature selection method is proposed using ReliefF and iterative neighborhood component analysis (RFINCA) to solve the feature selection problem. The goals of the RFINCA are to choose the optimal number of features automatically and use the effectiveness of ReliefF and neighborhood component analysis (NCA) together. A driving fatigue EEG dataset was used as a testbed to denote the effectiveness of eighteen conventional classifiers. According to the experimental results, a highly accurate EEG classification approach is presented. The proposed method also reached 100.0% classification accuracy by using a k-nearest neighborhood classifier.</p

Approaches, applications, and challenges in physiological emotion recognition — a tutorial overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Psychophysiological indices of recognition memory

It has recently been found that during recognition memory tests participants’ pupils dilate more when they view old items compared to novel items. This thesis sought to replicate this novel ‘‘Pupil Old/New Effect’’ (PONE) and to determine its relationship to implicit and explicit mnemonic processes, the veracity of participants’ responses, and the analogous Event-Related Potential (ERP) old/new effect. Across 9 experiments, pupil-size was measured with a video-based eye-tracker during a variety of recognition tasks, and, in the case of Experiment 8, with concurrent Electroencephalography (EEG). The main findings of this thesis are that: - the PONE occurs in a standard explicit test of recognition memory but not in “implicit” tests of either perceptual fluency or artificial grammar learning; - the PONE is present even when participants are asked to give false behavioural answers in a malingering task, or are asked not to respond at all; - the PONE is present when attention is divided both at learning and during recognition; - the PONE is accompanied by a posterior ERP old/new effect; - the PONE does not occur when participants are asked to read previously encountered words without making a recognition decision; - the PONE does not occur if participants preload an “old/new” response; - the PONE is not enhanced by repetition during learning. These findings are discussed in the context of current models of recognition memory and other psychophysiological indices of mnemonic processes. It is argued that together these findings suggest that the increase in pupil-size which occurs when participants encounter previously studied items is not under conscious control and may reflect primarily recollective processes associated with recognition memory

Effects of Diversity and Neuropsychological Performance in an NFL Cohort

Objective: The aim of this study was to examine the effect of ethnicity on neuropsychological test performance by comparing scores of white and black former NFL athletes on each subtest of the WMS. Participants and Methods: Data was derived from a de-identified database in South Florida consisting of 63 former NFL white (n=28, 44.4%) and black (n=35, 55.6%) athletes (Mage= 50.38; SD= 11.57). Participants completed the following subtests of the WMS: Logical Memory I and II, Verbal Paired Associates I and II, and Visual Reproduction I and II. Results: A One-Way ANOVA yielded significant effect between ethnicity and performance on several subtests from the WMS-IV. Black athletes had significantly lower scores compared to white athletes on Logical Memory II: F(1,61) = 4.667, p= .035, Verbal Paired Associates I: F(1,61) = 4.536, p = .037, Verbal Paired Associates: II F(1,61) = 4.677, p = .034, and Visual Reproduction I: F(1,61) = 6.562, p = .013. Conclusions: Results suggest significant differences exist between white and black athletes on neuropsychological test performance, necessitating the need for proper normative samples for each ethnic group. It is possible the differences found can be explained by the psychometric properties of the assessment and possibility of a non-representative sample for minorities, or simply individual differences. Previous literature has found white individuals to outperform African-Americans on verbal and non-verbal cognitive tasks after controlling for socioeconomic and other demographic variables (Manly & Jacobs, 2002). This highlights the need for future investigators to identify cultural factors and evaluate how ethnicity specifically plays a role on neuropsychological test performance. Notably, differences between ethnic groups can have significant implications when evaluating a sample of former athletes for cognitive impairment, as these results suggest retired NFL minorities may be more impaired compared to retired NFL white athletes