Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
This dissertation focuses on the classification of one-dimensional and two-dimensional signals. The one-dimensional signal classification problem involves the classification of brain signals for identifying the emotional responses of human subjects under given drug conditions. A strategy is developed to accurately classify ERPs in order to identify human emotions based on brain reactivity to emotional, neutral, and cigarette-related stimuli in smokers. A multichannel spatio-temporal model is employed to overcome the curse of dimensionality that plagues the design of parametric multivariate classifiers for multi-channel ERPs. The strategy is tested on the ERPs of 156 smokers who participated in a smoking cessation program. One half of the subjects were given nicotine patches and the other half were given placebo patches. ERPs were collected from 29 channel in response to the presentation of the pictures with emotional (pleasant and unpleasant), neutral/boring, and cigarette-related content. It is shown that human emotions can be classified accurately and the results also show that smoking cessation causes a drop in the classification accuracies of emotions in the placebo group, but not in the nicotine patch group. Given that individual brain patterns were compared with group average brain patterns, the findings support the view that individuals tend to have similar brain reactions to different types of emotional stimuli. Overall, this new classification approach to identify differential brain responses to different emotional types could lead to new knowledge concerning brain mechanisms associated with emotions common to most or all people. This novel classification technique for identifying emotions in the present study suggests that smoking cessation without nicotine replacement results in poorer differentiation of brain responses to different emotional stimuli. Future, directions in this area would be to use these methods to assess individual differences in responses to emotional stimuli and to different drug treatments. Advantages of this and other brain-based assessment include temporal precision (e.g, 400-800 ms post stimulus), and the elimination of biases related to self-report measures. The two-dimensional signal classification problems include the detection of graphite in testing documents and the detection of fraudulent bubbles in test sheets. A strategy is developed to detect graphite responses in optical mark recognition (OMR) documents using inexpensive visible light scanners. The main challenge in the formulation of the strategy is that the detection should be invariant to the numerous background colors and artwork in typical optical mark recognition documents. A test document is modeled as a superposition of a graphite response image and a background image. The background image in turn is modeled as superposition of screening artwork, lines, and machine text components. A sequence of image processing operations and a pattern recognition algorithm are developed to estimate the graphite response image from a test document by systematically removing the components of the background image. The proposed strategy is tested on a wide range of scanned documents and it is shown that the estimated graphite response images are visually similar to those scanned by very expensive infra-red scanners currently employed for optical mark recognition. The robustness of the detection strategy is also demonstrated by testing a large number of simulated test documents. A procedure is also developed to autonomously determine if cheating has occurred by detecting the presence of aberrant responses in scanned OMR test books. The challenges introduced by the significant imbalance in the numbers of typical and aberrant bubbles were identified. The aberrant bubble detection problem is formulated as an outlier detection problem. A feature based outlier detection procedure in conjunction with a one-class SVM classifier is developed. A multi-criteria rank-of-rank-sum technique is introduced to rank and select a subset of features from a pool of candidate features. Using the data set of 11 individuals, it is shown that a detection accuracy of over 90% is possible. Experiments conducted on three real test books flagged for suspected cheating showed that the proposed strategy has the potential to be deployed in practice.
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