Peter Boord

DEVELOPMENT OF AN ALGORITHM FOR AN EEG-BASED DRIVER FATIGUE COUNTERMEASURE

PROBLEM Fatigue affects a drivers ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability. METHOD Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue. RESULTS The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01). DISCUSSION This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system. IMPACT ON INDUSTRY The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.

A controlled investigation into the psychological determinants of fatigue.

Driver fatigue is associated with risks of road accidents that result in injury and death. Research has been limited by several issues such as confusion over definitions, how best to measure fatigue, and the contribution of psychological factors to fatigue. This study addressed these limitations by investigating the relationship between psychological factors and fatigue. Participants were assessed and were required to perform a monotonous task till they tired. Results found few psychological factors to be related to physiological and performance decrement fatigue outcome measures. However, psychological factors were found to correlate consistently with self-reported fatigue. The results suggest that fatigue is associated with a predisposition to be anxious, depressive, less self-assured, more conscientious (rule bound), less socially bold, less adaptable and low vigour. The results indicate that future research should employ a range of fatigue outcome measures in order to best understand what factors contribute to fatigue.

Levels of brain wave activity (8–13 Hz) in persons with spinal cord injury

Study design: Brain wave activity in people with spinal cord injury (SCI) was compared to brain wave activity in able-bodied controls.Objectives: To investigate whether a spinal injury results in changes in levels of brain wave activity in the 8–13 Hz spectrum of the electroencephalography (EEG).Setting: Sydney, Australia.Methods: Monopolar, multichannel EEG assessment was completed for 20 persons with SCI and 20 able-bodied, sex- and age-matched controls. A total of 14 channels of EEG were measured across the entire scalp for all participants. Comparisons between the able-bodied and SCI participants were made across the frontal, central, parietal, occipital and temporal regions. Comparisons were also made for impairment level, that is, between participants with tetraplegia and paraplegia.Results: Compared to the able-bodied controls, consistently reduced brain wave activity (measured by magnitude and peak amplitude) in the 8–13 Hz component of the EEG occurred in persons with SCI across all regions and sites, and differences were larger in the central, parietal and occipital sites. The SCI group also had consistently lower frequencies than the able-bodied controls. Furthermore, the subgroup of SCI participants with tetraplegia generally had significantly reduced brain activity (magnitude and peak amplitude) compared with the paraplegic subgroup and able-bodied controls.Conclusions: The findings of this research enhance our understanding of changes in brain wave activity that could be associated with deafferentation that occurs following SCI, as well as provide essential data on the potential of SCI persons to use a ‘hands free’ environmental control system that is based upon 8–13 Hz brain activity.

Using independent component analysis to remove artifact from electroencephalographic measured during stuttered speech

The electro-encephalographic (EEG) activity of people who stutter could provide invaluable information about the association of neural processing and stuttering. However, the EEG has never been adequately studied during speech in which stuttering naturally occurs. This is owing, in part, to the masking of the EEG signal by artifact from sources such as the speech musculature and from ocular activity. The aim of this paper was to demonstrate the ability of independent component analysis (ICA) to remove artifact from the EEG of stuttering children recorded while they are speaking and stuttering. The EEG of 16 male children who stuttered and 16 who did not stutter was recorded during a reading task. The recorded EEG that contained artifact was then subjected to ICA. The results demonstrated that the EEG assessed during stuttered speech had substantially more noise than the EEG of speech that did not contain stuttering (p<0.01). Furthermore, it was shown that ICA could effectively remove this artifact in all 16 children (p<0.01). The results from one child highlight the findings that ICA can be used to remove dominant artifact that has prevented the study of EEG activity during stuttered speech in children.

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.

Discrimination of left and right leg motor imagery for brain–computer interfaces

This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The results demonstrate that MMI of the left and right leg produce a contralateral preponderance of EEG alpha band desynchronization, which can be spatially discriminated. This suggests that lower extremity MMI could provide signals for additional BCI channels. The study also shows that movement imitation enhances alpha band desynchronization during MMI, and might provide a useful aid in the identification and training of BCI signals.

ALTERED BRAIN WAVE ACTIVITY IN PERSONS WITH CHRONIC SPINAL CORD INJURY

This study investigated brain wave activity associated with spinal cord injury (SCI). Electroencephalograms (EEG) were compared between 10 individuals with SCI and 10 age and sex matched able-bodied controls using a 64-channel EEG montage. SCI participants had chronic (>12 months) paraplegic clinically complete injuries. The 64 channels of EEG data were spread diffusely over the cortex and were compared for delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) wave components of the EEG frequency spectra. No significant magnitude or directional changes were found in the delta (2–4 Hz) or theta (4–8 Hz) wave frequency bands between these two groups. However, significant and consistent decreased alpha wave (8–13 Hz) and increased beta wave activity (13–30 Hz) were found in the SCI participants across the cortex compared to the able-bodied control group. These findings suggest that the SCI group have increased neural processing compared to the able-bodied individuals, which may be related to ongoing reorganization of brain structures following SCI.

Brain–Computer Interface—FES Integration: Towards a Hands-free Neuroprosthesis Command System

This paper presents a critical review of brain–computer interfaces (BCIs) and their potential for neuroprosthetic applications. Summaries are provided for the command interface requirements of hand grasp, multijoint, and lower extremity neuroprostheses, and the characteristics of various BCIs are discussed in relation to these requirements. The review highlights the current limitations of BCIs and areas of research that need to be addressed to enhance BCI—FES integration.

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

Brain-Computer Interface-FES Integration: Towards a Hands-free Neuroprosthesis Command System: Brain-Computer Interface-FES Integration

This paper presents a critical review of brain–computer interfaces (BCIs) and their potential for neuroprosthetic applications. Summaries are provided for the command interface requirements of hand grasp, multijoint, and lower extremity neuroprostheses, and the characteristics of various BCIs are discussed in relation to these requirements. The review highlights the current limitations of BCIs and areas of research that need to be addressed to enhance BCI—FES integration.