Ranjit Arulnayagam Thuraisingham

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.

Improving correct switching rates in a 'hands-free' environmental control system

One potential negative impact on the quality of life of a spinal cord injured person is the loss of the ability to control devices in their immediate environment. Consequently, research and development has been conducted on technology designed to restore some measure of independence by providing means of control over these devices. A previous assistive device using changes in brain signals from eye closure as its switching system was created. Brain signals were processed using spectral analysis and although this was a successful technique, there were limitations that resulted in higher than desired switching errors. This paper presents results of an alternative method for processing brain signals as the basis for switching, called fractal dimension. In comparison to the spectral technique, the fractal dimension technique was successful in reducing the number of false positive and false negative errors. Additionally, it eliminated the need for a baseline setup for this system. This suggests that fractal dimension is a potentially viable method for analysing brain signals for use in assistive control systems.

Estimation of Teager energy using the Hilbert–Huang transform

A new method to estimate the Teager energy (TE) is presented here which uses an instantaneous energy expression and the Hilbert–Huang transform (HHT). The energy expression depends on the square of the instantaneous amplitude and digital frequency of the signal. This energy of the signal is estimated from the instantaneous energies of the intrinsic mode functions (IMFs), obtained from the HHT. The energy expression used for the TE ensures that the energy is always positive, and it is in agreement with the energy required to generate a sinusoid. It avoids the limitations in the use of the discrete TE operator (TEO), where for the output of the TEO to be positive and to give energies which match the energy required to generate a sinusoid, the signal must satisfy certain conditions. Numerical study on a data set of neuro-signals shows that these problems persist even when TEO is applied to the IMFs obtained from the empirical mode decomposition of the signal. Such a procedure is used in the Teager–Huang transform. There is a sharp drop in the number of negative values when IMFs are used, but the number is still not zero.

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 ones brain activity as a control mechanism to control devices in the persons 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

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 (IMFs); and the Hilbert transform of the IMFs. 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 IMFs. This result has implications for assistive technology that rely on EEG changes in EO and EC states.

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.

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.

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.

Using S-transform in EEG analysis for measuring an alert versus mental fatigue state.

This paper presents research that investigated the effects of mental fatigue on brain activity using electroencephalogram (EEG) signals. Since EEG signals are considered to be non-stationary, time-frequency analysis has frequently been used for analysis. The S-transform is a time-frequency analysis method and is used in this paper to analyze EEG signals during alert and fatigue states during a driving simulator task. Repeated-measure MANOVA results show significant differences between alert and fatigue states within the alpha (8-13Hz) frequency band. The two sites demonstrating the greatest increases in alpha activity during fatigue were the Cz and P4 sites. The results show that S-transform analysis can be used to distinguish between alert and fatigue states in the EEG and also supports the use of the S-transform for EEG analysis.

Switching rate changes associated with mental fatigue for assistive technologies

This paper presents research that investigated the effects of mental fatigue on brain activity associated with eyes open and eyes closed conditions. The changes associated with electroencephalography (EEG) alpha wave activity (8–13Hz) during eye closure has previously been shown to be an effective strategy for switching and activating devices as an environmental control system (ECS) designed for people with severe disability like spinal cord injury (SCI). The results showed that switching times did increase due to fatigue, however, these increases were not large (around 1 second longer to switch) and this difference was not significant. When baselines were readjusted taking into account the change in alpha wave activity due to the fatigue, switching reduced to times typically seen when the person was alert. Error rates were similar between the alert and fatigue sates. Implications of these results for a hands-free ECS are discussed.