10 research outputs found
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Coherence analysis: Methods, solutions and problems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A coherence function is a measure of the correlation of two signals and may
be used as a measure for functional relationship between brain areas. In
studying functional relationships, referenced EEG (REEG) coherence analysis
yields important new aspects of brain activities, which complement the
data obtained by power spectral analysis. However, REEG-based coherence tends to show a false high value due to volume conduction from un correlated sources (VCUS). Existing signal processing methods address this issue using a Fourier coherence function of scalp Laplacian. Although this method has been proved useful to reveal correlation between EEG signals with minimum VCUS effects, it only provides frequency-domain analysis. Since EEG signals are highly non-stationary, it is more appropriate to use time-frequency
methods for coherence analysis of scalp Laplacian. Thus this research applies the wavelet transform on coherence analysis of scalp Laplacian. To verify our technique, already recorded EEG data of event related potentials were obtained from a study of two large groups of alcoholic and abstinent alcoholic subjects, performing visual picture-recognition tasks. The proposed coherence method successfully detected time-frequency correlation between EEG signals with minimum VCUS effects. It showed significant spatial specificity and revealed detailed coherence patterns. Some new important results regarding time-frequency characteristics of VCUS effects on wavelet
and short-time Fourier transform (STFT) coherence analysis of REEG signals were deduced. The proposed coherence method was also compared to a conventional wavelet coherence method of REEG signals in the study of coherence difference between coherences of alcoholic and abstinent alcoholic EEG signals. Results of this study provided substantial evidence that VCUS
effects are not additive and therefore can not be ignored in comparison of different brain states between groups of subjects
A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy
This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface.
A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes.
The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty.
It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763)