Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task.

Driver fatigue is a prevalent problem and a major risk for road safety accounting for approximately 20-40% of all motor vehicle accidents. One strategy to prevent fatigue related accidents is through the use of countermeasure devices. Research on countermeasure devices has focused on methods that detect physiological changes from fatigue, with the fast temporal resolution from brain signals, using the electroencephalogram (EEG) held as a promising technique. This paper presents the results of nonlinear analysis using sample entropy and second-order difference plots quantified by central tendency measure (CTM) on alert and fatigue EEG signals from a driving simulated task. Results show that both sample entropy and second-order difference plots significantly increases the regularity and decreases the variability of EEG signals from an alert to a fatigue state

Effects of mental fatigue on 8-13Hz brain activity in people with spinal cord injury.

Brain computer interfaces (BCIs) can be implemented into assistive technologies to provide 'hands-free' control for the severely disabled. BCIs utilise voluntary changes in one's brain activity as a control mechanism to control devices in the person's immediate environment. Performance of BCIs could be adversely affected by negative physiological conditions such as fatigue and altered electrophysiology commonly seen in spinal cord injury (SCI). This study examined the effects of mental fatigue from an increase in cognitive demand on the brain activity of those with SCI. Results show a trend of increased alpha (8-13Hz) activity in able-bodied controls after completing a set of cognitive tasks. Conversely, the SCI group showed a decrease in alpha activity due to mental fatigue. Results suggest that the brain activity of SCI persons are altered in its mechanism to adjust to mental fatigue. These altered brain conditions need to be addressed when using BCIs in clinical populations such as SCI. The findings have implications for the improvement of BCI technology

Heart rate variability in critical care medicine: a systematic review.

BACKGROUND: Heart rate variability (HRV) has been used to assess cardiac autonomic activity in critically ill patients, driven by translational and biomarker research agendas. Several clinical and technical factors can interfere with the measurement and/or interpretation of HRV. We systematically evaluated how HRV parameters are acquired/processed in critical care medicine. METHODS: PubMed, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (1996-2016) were searched for cohort or case-control clinical studies of adult (>18 years) critically ill patients using heart variability analysis. Duplicate independent review and data abstraction. Study quality was assessed using two independent approaches: Newcastle-Ottowa scale and Downs and Black instrument. Conduct of studies was assessed in three categories: (1) study design and objectives, (2) procedures for measurement, processing and reporting of HRV, and (3) reporting of relevant confounding factors. RESULTS: Our search identified 31/271 eligible studies that enrolled 2090 critically ill patients. A minority of studies (15; 48%) reported both frequency and time domain HRV data, with non-normally distributed, wide ranges of values that were indistinguishable from other (non-critically ill) disease states. Significant heterogeneity in HRV measurement protocols was observed between studies; lack of adjustment for various confounders known to affect cardiac autonomic regulation was common. Comparator groups were often omitted (n = 12; 39%). This precluded meaningful meta-analysis. CONCLUSIONS: Marked differences in methodology prevent meaningful comparisons of HRV parameters between studies. A standardised set of consensus criteria relevant to critical care medicine are required to exploit advances in translational autonomic physiology.GLA is supported by a British Journal of Anaesthesia and Royal College of Anaesthetists Basic Science fellowship, British Oxygen Company grant from the Royal College of Anaesthetists and British Heart Foundation programme grant (RG/14/4/3073

Evaluating the efficacy of an automated procedure for EEG artifact removal

Electroencephalography (EEG) signals are often contaminated with artifacts arising from many sources such as those with ocular and muscular origins. Artifact removal techniques often rely on the experience of the EEG technician to detect these artifact components for removal. This paper presents the results comparing an automated procedure (AT) against visually (VT) choosing artifactual components for removal, using second order blind identification (SOBI) and canonical correlation analyses. The results show that the resulting EEG signal after artifact removal for the AT and VT were comparable using a technique that measures the variance amongst electrodes and spectral energy. The AT technique is objective, faster and easier to use, and shown here to be comparable to the standard technique of visually detecting artifact components. ©2009 IEEE

Using microstate intensity for the analysis of spontaneous EEG: Tracking changes from alert to the fatigue state

Fatigue is a negative symptom of many illnesses and also has major implications for road safety. This paper presents results using a method called microstate segmentation (MSS). It was used to distinguish changes from an alert to a fatigue state. The results show a significant increase in MSS instantaneous amplitude during the fatigue state. Plotting the linear gradient of the nonlinear part of the phase data from the MSS also showed a significant difference (P<0.01) in the gradients of the alert state compared to the fatigue state. The results suggest that MSS can be used in analyzing spontaneous electroencephalography (EEG) signals to detect changes in physiological states. The results have implications for countermeasures used in detecting fatigue. ©2009 IEEE

Analysis of eyes open, eye closed EEG signals using second-order difference plot

An assistive technology developed for "hands free" control of electrical devices to be used by severely impaired people within their environment, relies upon using signal processing techniques for analyzing eyes closed (EC) and eyes open (EO) states in the electroencephalography (EEG) signal. Here, we apply a signal processing technique used in continuous chaotic modeling to investigate differences in the EEG time series between EC and EO states. This method is used to detect the degree of variability from a second-order difference plot, and quantifying this using a central tendency measures. The study used EEG time series of EO and EC states from 33 able-bodied and 17 spinal cord injured participants. The results found an increased EEG variability in brain activity during EC compared to EO. This increased EEG variability occurred in the O2 electrode, which overlays the primary visual cortex V1, and could be a result of the replacement of the coherent information obtained during EO by noise. A continuous measure of the variability was then used to demonstrate that this technique has the potential to be used as a switching mechanism for assistive technologies. © International Federation for Medical and Biological Engineering 2007

Frequency analysis of eyes open and eyes closed EEG signals using the Hilbert-Huang Transform

Frequency analysis based on the Hilbert-Huang transform (HHT) is examined as an alternative to Fourier spectral analysis in the study of EEG signals. This method overcomes the need for the EEG signal to be linear and stationary, assumptions necessary for the application of Fourier spectral analysis. The HHT method comprises two components: empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMF's); and the Hilbert transform of the IMF's. This technique is applied here in the study of consecutive eyes open (EO), eyes closed (EC) EEG signals of able bodied and spinal cord injured participants. The study found that in this EO, EC pair the instantaneous frequencies in the EO state were higher compared to the EC state. The Hilbert weighted frequency, a measure of the mean of the instantaneous frequencies present in an IMF, is used here to detect these changes from EO to the EC state in an EEG signal. Although there was a good detection of this change with information obtained from just one IMF (94% in able-bodied persons and 84% in SCI persons), almost 100% success in detecting between group differences was achieved using all the IMF's. This result has implications for assistive technology that rely on EEG changes in EO and EC states. © 2012 IEEE

Detecting neural changes during stress and fatigue effectively: A comparison of spectral analysis and sample entropy

Brain computer interface (BCI) technology as Its name implies, relies upon decoding brain signals into operational commands. Aside from needing effective means of control, successful BCIs need to remain stable in varying physiological conditions. BCIs need to be developed with mechanisms to recognise and respond to physiological states (such as stress and fatigue) that can disrupt user capability. This paper compares a spectral analysis of EEG signals technique with a nonlinear method of sample entropy to detect changes In brain dynamics during moments of stress and fatigue. The results demonstrated few changes In the spectral frequency bands of the EEG during fatigue and stress conditions. However, when the EEG signals were analysed with the nonlinear technique of sample entropy the results indicated a reduction of complexity during moments of fatigue and stress and an increase In complexity during moments of engagement to the task. © 2007 IEEE

Using fractal analysis to improve switching rates in "hands free" environmental control technology for the severely disabled

A negative impact on the quality of life of the severely neurologically disordered such as spinal cord injured persons is the loss of the ability to control devices in their immediate environment. Consequently, we have conducted research on technology designed to restore some measure of independence by providing hands free control over these devices by using EEG signals associated with eye closure (EC) and eye opening (EO). In a previous study we demonstrated that the nonlinear technique fractal dimension analysis was a viable alternative to spectral analysis in detecting these signals in the EEG of able bodied persons. This paper explores the efficacy of using fractal dimension to detect EC/EO signals in a spinal cord injured population. The fractal dimension method was found to improve from the standard spectral analysis technique in that there was a significant reduction is the occurrence of false positive and false negative switching. This improved detection of EC/EO in the brain activity of severely disabled people will be utilised in our technology for remote switching of electrical devices. © 2007 IEEE