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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.

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.

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.

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.

Research and development has been conducted on a "hands-free" brain wave activity (electroencephalography-based) assistive technology device called the Mind Switch. It has been designed to provide a severely disabled person (such as someone with tetraplegia) with the ability to switch using their brain activity and thus activate devices in the environment. An area of concern has been the time it takes to complete set-up of the system before individual use, as well as the occurrence of errors in switching rates. In order to improve the user friendliness of the system, this study compared the efficacy of detecting electroencephalography (EEG) changes in brain signals that occur with eye closure and used as a "switch", with two different methods of processing the EEG. Using data from spinal cord injured participants, fractal analysis, that is, the change in fractal dimension of the EEG wave during eye closure was compared to the original method of using spectral analysis change. In comparison to the spectral technique, the fractal dimension technique improved set up time, and was substantially improved in reducing the number of false positive and false negative errors when activating the device. Furthermore, the time to switch was also marginally reduced when using fractal dimension processing. Implications for improving the viability of an EEG based environmental control system for the severely neurologically injured are discussed. [ABSTRACT FROM AUTHOR]