Generalizability of Frequency Weighting Curve for Extraction of Spectral Drowsy Component From the EEG Signals Recorded in Eyes-Closed Condition

Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited. As such, the present study psychophysiologically examined monotonous driving, fatigue, and sleepiness in a group of passenger train drivers and a group of non-professional drivers. Sixty-three train drivers and thirty non-professional drivers participated in the present study, which captured 32-lead electroencephalogram (EEG) data during a monotonous driving task. Fatigue and sleepiness were self-evaluated using the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Karolinksa Sleepiness Scale, and the Checklist of Individual Strength. Unexpectedly, fatigue and sleepiness scores did not significantly differ between the groups; however, train drivers generally scored lower than non-professional drivers, which may be indicative of individual and/or industry attempts to reduce fatigue. Across both groups, fatigue and sleepiness scores were negatively correlated with theta, alpha, and beta EEG variables clustered towards the fronto-central and temporal regions. Broadly, these associations may reflect a monotony-associated blunting of neural activity that is associated with a self-reported fatigue state.

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual’s cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task.

Frequency spectrum recurrence analysis

In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.

Inside the clinical evaluation of sleepiness: subjective and objective tools

PURPOSE

To critically review the available tools for evaluating excessive daytime sleepiness (EDS) in clinical practice.

METHODS

Objective tests and subjective scales were divided into three groups in accordance with the different dimensions of sleepiness they measure, namely physiological, manifest, and introspective. Strengths, weaknesses, and limitations of each test have been analysed and discussed along with the available recommendations for their use in clinical practice.

RESULTS

The majority of the tests developed for sleepiness evaluation do not have practical usefulness outside the research setting. The suboptimal correlation between different tests mainly depends on the different dimensions of sleepiness they analyse. Most importantly in-laboratory tests poorly correlate with sleepiness in real-life situations and, to date, none is able to predict the risk of injuries related to EDS, especially on an individual level.

CONCLUSIONS

There exists not the one best test to assess EDS, however, clinicians can choose a more specific test to address a specific diagnostic challenge on the individual level. The development of novel performance tests with low cost and easy to administer is advisable for both screening purposes and fitness for duty evaluations in populations at high risk of EDS-related injuries, for example professional drivers.

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers

OBJECTIVE

In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers.

APPROACH

Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20).

MAIN RESULTS

Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores.

SIGNIFICANCE

Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.

Association of an individual's ability to overcome desire to fall asleep with a higher anterior-posterior gradient in electroencephalographic indexes of sleep pressure

Individual differences in ability to overcome desire to fall asleep cannot be accurately predicted from subjective and objective measurements of sleepiness level. Previously, we showed that an exponential buildup of sleep pressure during prolonged wakefulness can be accurately traced with electroencephalographic (EEG) indexes, such as Spectral Sleep Pressure Component (SSPC) score and score on the 2nd principal component (2PC) of the EEG spectrum. The anterior-posterior gradients in SSPC and 2PC scores were calculated as the differences between frontal and occipital scores and examined as possible correlates of individual's ability to overcome desire of falling asleep. Fifteen young and 15 older adults participated in two identically designed sleep deprivation experiments. After, at least, 12hours of wakefulness, resting EEG recordings were obtained from frontal and occipital derivations with 2-h intervals during 26-50hours. Due to irresistible desire to sleep, 11 young and 5 older adults completed <25 required EEG recordings. SSPC and 2PC scores were computed and, by subtracting occipital scores from frontal scores, the anterior-posterior gradients in SSPC and 2PC scores were calculated on one-min intervals of 5-min eyes closed EEG records. The analysis of these anterior-posterior gradients revealed their age-related difference and association with the number of completed EEG recording sessions (13-25). This association remained significant after accounting for age, alertness-sleepiness level, minute of eyes closed recording, and day of experiment. It seems that the anterior-posterior gradients in the EEG indexes of sleep pressure are the objective correlates of individual's ability to overcome desire to fall asleep.