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Crossen, Samantha LokelaniInvestigation of Variability in Cognitive State Assessment based on Electroencephalogram-derived Features
Master of Science in Engineering (MSEgr), Wright State University, 2011, Biomedical Engineering
To implement adaptive aiding in modern aviation systems there is a need for accurate and reliable classification of cognitive workload. Using electroencephalogram (EEG)-derived features, it has been reported that an Artificial Neural Network (ANN) can achieve 95% or higher classification accuracy on the same day for an individual operator, but only 70% or less on a different day. To gain a further insight into this discrepancy, data from a previous study was utilized to study the classification variability. The EEG-derived features were first calculated by spectral power estimation. The variability was then analyzed by performing cognitive workload classification in which different methods of training and testing were used and different classifiers were implemented to compare classification accuracies. The classifiers include an ANN, Adaboost Algorithm, and a t-test method. The results show that when the ANN or Adaboost method is used, the amount of overlapping among training and testing data impacts the classification accuracy significantly. When there is no overlap, all classifiers can only achieve an accuracy of about 70%, with the Adaboost outperforming other classifiers slightly. By allowing some overlap, the accuracy of the ANN or Adaboost method increases significantly. It was concluded that the main source of the classification variability is the inherent variability of the EEG-derived features.

Committee:

Ping He, PhD (Advisor); James Christensen, PhD (Committee Member); Yan Liu, PhD (Committee Member)

Subjects:

Biomedical Research

Keywords:

Electroencephalogram (EEG); Artificial Neural Network (ANN); AdaBoost Algorithm; Workload Classification; Feature Variability

Mahadevan, AnandiReal Time Ballistocardiogram Artifact Removal in EEG-fMRI Using Dilated Discrete Hermite Transform
Master of Science in Engineering, University of Akron, 2008, Biomedical Engineering
Electroencephalogram (EEG) signals, when recorded within the strong magnetic field of an MR scanner, are subject to various artifacts, of which the ballistocardiogram (BCG) is one of the prominent artifacts affecting the quality of the EEG. The BCG is continuously varying with time and its spectrum overlaps with the EEG spectra, making its suppression a signal processing challenge. A novel method for the identification and removal of this artifact using shape basis functions of the new dilated discrete Hermite transform is investigated in this paper. The BCG artifacts are modeled for every heart beat, using these discrete Hermite basis functions, and are subsequently subtracted from the ongoing EEG. Experimental EEG data was recorded inside and outside a 3 Tesla MRI scanner, from a total of 7 subjects under various experimental conditions. Quantitative assessment of the efficiency of this algorithm was evaluated by adding known BCG templates, at varying Signal to Noise Ratios (SNRs), to the EEG recorded outside the scanner. Significant reduction of the BCG artifact (p<0.05) was obtained without distortion of the underlying EEG signal. This method was compared with current BCG artifact removal techniques using EEG data recorded within the scanner field and its performance was found to be better than the Average Artifact Subtraction (AAS) method and had comparable results to the Independent Component Analysis (ICA) based methods. Real time implementation of the algorithm is possible due to the ease of computations.

Committee:

Dale H. Mugler, PhD (Advisor)

Subjects:

Biomedical Research

Keywords:

Ballistocardiogram (BCG); Electroencephalogram (EEG); functional Magnetic Resonance Imaging (fMRI); Average Artifact Subtraction (AAS); Independent Component Analysis (ICA)

Ghosh Dastidar, SamanwoyModels of EEG data mining and classification in temporal lobe epilepsy: wavelet-chaos-neural network methodology and spiking neural networks
Doctor of Philosophy, The Ohio State University, 2007, Biomedical Engineering
A multi-paradigm approach integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks is developed for EEG-based epilepsy diagnosis and seizure detection. This research challenges the assumption that the EEG represents the dynamics of the entire brain as a unified system. It is postulated that the sub-bands yield more accurate information about constituent neuronal activities underlying the EEG. Consequently, certain changes in EEGs not evident in the original full-spectrum EEG may be amplified when each sub-band is analyzed separately. A novel wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma sub-bands of EEGs for detection of seizure and epilepsy. The methodology is applied to three different groups of EEGs: healthy subjects, epileptic subjects during a seizure-free interval (interictal), and epileptic subjects during a seizure (ictal). Two potential markers of abnormality quantifying the non-linear chaotic EEG dynamics are discovered: the correlation dimension and largest Lyapunov exponent. A novel wavelet-chaos-neural network methodology is developed for EEG classification. Along with the aforementioned two parameters, the standard deviation (quantifying the signal variance) is employed for EEG representation. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy (96.7%). To increase the robustness of classification, a novel principal component analysis-enhanced cosine radial basis function neural network classifier is developed. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network employed in the second stage significantly. The new classifier is as accurate as LMBPNN and is twice as robust. Next, biologically realistic artificial neural networks are developed to reach the next milestone in artificial intelligence. First, an efficient spiking neural network (SNN) model is presented using three training algorithms: SpikeProp, QuickProp, and RProp. Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy. Next, a new Multi-Spiking Neural Network (MuSpiNN) and supervised learning algorithm (Multi-SpikeProp) are developed. Finally, the models are applied to the epilepsy and seizure detection problems to achieve high classification accuracies.

Committee:

Hojjat Adeli (Advisor)

Keywords:

Temporal Lobe Epilepsy; Electroencephalogram (EEG); EEG Classification; Epilepsy Diagnosis; Seizure Detection; Wavelet Transform; Chaos Theory; Artificial Neural Networks; Spiking Neural Networks; Principal Component Analysis; Cosine Radial Basis Function