Subdural electroencephalogram analysis for extracting discriminating measures in epileptogenic data

This dissertation introduces an integrated algorithm for a new application dedicated at discriminating between electrodes leading to a seizure onset and those that do not, using interictal subdural EEG data. The significance of this study is in determining among all of these channels, all containing interictal spikes, why some electrodes eventually lead to seizure while others do not. A first finding in the development process of the algorithm is that these interictal spikes had to be asynchronous and should be located in different regions of the brain, before any consequential interpretations of EEG behavioral patterns are possible. A singular merit of the proposed approach is that even when the EEG data is randomly selected (independent of the onset of seizure), we are able to classify those channels that lead to seizure from those that do not. It is also revealed that the region of ictal activity does not necessarily evolve from the tissue located at the channels that present interictal activity, as commonly believed.^ The study is also significant in terms of correlating clinical features of EEG with the patient's source of ictal activity, which is coming from a specific subset of channels that present interictal activity. The contributions of this dissertation emanate from (a) the choice made on the discriminating parameters used in the implementation, (b) the unique feature space that was used to optimize the delineation process of these two type of electrodes, (c) the development of back-propagation neural network that automated the decision making process, and (d) the establishment of mathematical functions that elicited the reasons for this delineation process. ^

A study of indicators discriminating between the recipients and non -recipients of the International Baccalaureate Diploma

This study examined relationships among variables in the Pre-International Baccalaureate (Pre-IB) Program admissions criteria and the Pre-IB Program course grades to discriminate between recipient and non recipient groups of the International Baccalaureate (IB) Diploma award. The study involved a multiracial sample of 142 IB Diploma graduates between the years 1992 and 1996 from one IB magnet school. The IB school is located within an urban high school of a predominantly Black student enrollment. A discriminant function analysis found that the highest correlations between predictors and the discriminant function were 9th- and 10th-grade mathematics and 10th-grade science course grades. Ninth-grade course grades of science, 9th-grade and 10th-grade course grades of English, foreign language, and social studies, and 7th-grade Iowa Tests of Basic Skills (ITBS) Reading Comprehension scores were also highly correlated to the discriminant function. The ITBS Battery and subscores of Vocabulary, Total Language, Total Work-Study, and Total Mathematics subscores in seventh grade and a grade point average from language arts, social studies, science, and mathematics in seventh grade were not highly correlated to the discriminant function. Recommendations were presented in the areas of curriculum and instruction, guidance services, student mentoring, and decision-making processes which would parallel the IB examination procedure and thereby enhance the alignment of the IB Program enabling more students to become recipients of the IB Diploma award. ^

A Serological and Disc Electrophoretic Study of North American Typha

Pollen and seed proteins of seven selected North American and Puerto Rican Typha populations were compared using two serological methods and disc electrophoresis. These methods were capable of discriminating among all taxa studied: Typha latifolia, T. angustifolia, T. X glauca, and T. domingensis. The two hybrid populations were found to contain proteins not found in either parent. Typha domingensis was serologically the most distinct of the four taxa. The diagnostic morphological characteristics for Typha species were studied in all populations, and statistical comparisons are presented. Data from the morphological observations agreed with the information obtained from the chemosystematic research. All data indicate that the three taxa should be maintained as separate species. The hybrid nature of the putative T. X glauca is verified by both the biochemical and morphological data. Observed morphological and biochemical differences support taxonomic treatments in which T. domingensis is designated as a separate species.

Cognitive dissonance and the effect of acceptance of methodology, beliefs about religion, religious orientation, and dogmatism on the success of university students in a religious studies course

The problem investigated was negative effects on the ability of a university student to successfully complete a course in religious studies resulting from conflict between the methodologies and objectives of religious studies and the student's system of beliefs. Using Festinger's theory of cognitive dissonance as a theoretical framework, it was hypothesized that completing a course with a high level of success would be negatively affected by (1) failure to accept the methodologies and objectives of religious studies (methodology), (2) holding beliefs about religion that had potential conflicts with the methodologies and objectives (beliefs), (3) extrinsic religiousness, and (4) dogmatism. The causal comparative method was used. The independent variables were measured with four scales employing Likert-type items. An 8-item scale to measure acceptance of the methodologies and objectives of religious studies and a 16-item scale to measure holding of beliefs about religion having potential conflict with the methodologies were developed for this study. These scales together with a 20-item form of Rokeach's Dogmatism Scale and Feagin's 12-item Religious Orientation Scale to measure extrinsic religiousness were administered to 144 undergraduate students enrolled in randomly selected religious studies courses at Florida International University. Level of success was determined by course grade with the 27% of students receiving the highest grades classified as highly successful and the 27% receiving the lowest grades classified as not highly successful. A stepwise discriminant analysis produced a single significant function with methodology and dogmatism as the discriminants. Methodology was the principal discriminating variable. Beliefs and extrinsic religiousness failed to discriminate significantly. It was concluded that failing to accept the methodologies and objectives of religious studies and being highly dogmatic have significant negative effects on a student's success in a religious studies course. Recommendations were made for teaching to diminish these negative effects.

A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Detecting deception: Identifying differences in liars' and truth tellers' verbal strategies

Deception research has traditionally focused on three methods of identifying liars and truth tellers: observing non-verbal or behavioral cues, analyzing verbal cues, and monitoring changes in physiological arousal during polygraph tests. Research shows that observers are often incapable of discriminating between liars and truth tellers with better than chance accuracy when they use these methods. One possible explanation for observers' poor performance is that they are not properly applying existing lie detection methods. An alternative explanation is that the cues on which these methods — and observers' judgments — are based do not reliably discriminate between liars and truth tellers. It may be possible to identify more reliable cues, and potentially improve observers' ability to discriminate, by developing a better understanding of how liars and truth tellers try to tell a convincing story. ^ This research examined (a) the verbal strategies used by truthful and deceptive individuals during interviews concerning an assigned activity, and (b) observers' ability to discriminate between them based on their verbal strategies. In Experiment I, pre-interview instructions manipulated participants' expectations regarding verifiability; each participant was led to believe that the interviewer could check some types of details, but not others, before deciding whether the participant was being truthful or deceptive. Interviews were then transcribed and scored for quantity and type of information provided. In Experiment II, observers listened to a random sample of the Experiment I interviews and rendered veracity judgments; half of the observers were instructed to judge the interviews according to the verbal strategies used by liars and truth tellers and the other half were uninstructed. ^ Results of Experiment I indicate that liars and truth tellers use different verbal strategies, characterized by a differential amount of detail. Overall, truthful participants provided more information than deceptive participants. This effect was moderated by participants' expectations regarding verifiability such that truthful participants provided more information only with regard to verifiable details. Results of Experiment II indicate that observers instructed about liars' and truth tellers' verbal strategies identify them with greater accuracy than uninstructed observers. ^

The discrimination and association of float glass and the quantitative analysis of liquids from aerosols and microdrops using laser induced breakdown spectroscopy

Glass is a common form of trace evidence found at many scenes of crimes in the form of small fragments. These glass fragments can transfer to surrounding objects and/or persons and may provide forensic investigators valuable information to link a suspect to the scene of a crime. Since the elemental composition of different glass sources can be very similar, a highly discriminating technique is required to distinguish between fragments that have originated from different sources. ^ The research presented here demonstrates that Laser Induced Breakdown Spectroscopy (LIBS) is a viable analytical technique for the association and discrimination of glass fragments. The first part of this research describes the optimization of the LIBS experiments including the use of different laser wavelengths to investigate laser-material interaction. The use of a 266 nm excitation laser provided the best analytical figures of merit with minimal damage to the sample. The resulting analytical figures of merit are presented. The second part of this research evaluated the sensitivity of LIBS to associate or discriminate float glass samples originating from the same manufacturing plants and produced at approximately the same time period. Two different sample sets were analyzed ranging in manufacturing dates from days to years apart. Eighteen (18) atomic emission lines corresponding to the elements Sr, K, Fe, Ca, Al, Ba, Na, Mg and Ti, were chosen because of their detection above the method detection limits and for presenting differences between the samples. Ten elemental ratios producing the most discrimination were selected for each set. When all the ratios are combined in a comparison, 99% of the possible pairs were discriminated using the optimized LIBS method generating typical analytical precisions of ∼5% RSD. ^ The final study consisted of the development of a new approach for the use of LIBS as a quantitative analysis of ultra-low volume solution analysis using aerosols and microdrops. Laser induced breakdown spectroscopy demonstrated to be an effective technique for the analysis of as low as 90 pL for microdrop LIBS with 1 pg absolute LOD and 20 µL for aerosol LIBS with an absolute LOD of ∼100 fg.^

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.

Determination of signature volatile odor chemicals emanating from novel biological specimens by non-invasive analytical techniques for the potential use in forensic identifications

Human scent, or the volatile organic compounds (VOCs) produced by an individual, has been recognized as a biometric measurement because of the distinct variations in both the presence and abundance of these VOCs between individuals. In forensic science, human scent has been used as a form of associative evidence by linking a suspect to a scene/object through the use of human scent discriminating canines. The scent most often collected and used with these specially trained canines is from the hands because a majority of the evidence collected is likely to have been handled by the suspect. However, the scents from other biological specimens, especially those that are likely to be present at scenes of violent crimes, have yet to be explored. Hair, fingernails and saliva are examples of these types of specimens. ^ In this work, a headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) technique was used for the identification of VOCs from hand odor, hair, fingernails and saliva. Sixty individuals were sampled and the profiles of the extracted VOCs were evaluated to assess whether they could be used for distinguishing individuals. Preliminary analysis of the biological specimens collected from an individual (intra-subject) showed that, though these materials have some VOCs in common, their overall chemical profile is different for each specimen type. Pair-wise comparisons, using Spearman Rank correlations, were made between the chemical profiles obtained from each subject, per a specimen type. Greater than 98.8% of the collected samples were distinguished from the subjects for all of the specimen types, demonstrating that these specimens can be used for distinguishing individuals. ^ Additionally, field trials were performed to determine the utility of these specimens as scent sources for human scent discriminating canines. Three trials were conducted to evaluate hair, fingernails and saliva in comparison to hand odor, which was considered the standard source of human odor. It was revealed that canines perform similarly to these alternative human scent sources as they do to hand odor implying that, though there are differences in the chemical profiles released by these specimens, they can still be used for the discrimination of individuals by trained canines.^

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

Waveform Detection of Conspecifics in the Weakly Electric Fish Gymnotus Carapo

Weakly electric fish use electric fields for communication and location of objects. Electroreceptors that are located around the mouth and along the length of the body are used in order to "decode" the electric organ discharge (EOD). The knollenorgan in Mormyriformes aids in distinguishing between different EODs. Gymnotiformes, however, have no such electroreceptors. How then are Gyrnnotiformes distinguishing between conspecific EODs? In this study scan sampling was investigated to determine whether Gymnotus carapo uses this mechanism to differentiate between distinct EODs. After determining whether Gymnotus carapo was discriminating between neighbor and stranger EODs, these same EODs were played to the test fish either jittered (the EOD of the test fish and that of the playback could not coincide) or non-jittered (the two EODs could coincide). The results show that the test fish was not discriminating between neighbor and stranger EODs. Thus, conclusions about the use of scan sampling by Gymnotus carapo to distinguish between EODs cannot be made.

A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Analysis of White Latex Paints using Laser Induced Breakdown Spectroscopy for Forensic Applications

The analysis of white latex paint is a problem for forensic laboratories because of difficulty in differentiation between samples. Current methods provide limited information that is not suitable for discrimination. Elemental analysis of white latex paints has resulted in 99% discriminating power when using LA-ICP-MS; however, mass spectrometers can be prohibitively expensive and require a skilled operator. A quick, inexpensive, effective method is needed for the differentiation of white latex paints. In this study, LIBS is used to analyze 24 white latex paint samples. LIBS is fast, easy to operate, and has a low cost. Results show that 98.1% of variation can be accounted for via principle component analysis, while Tukey pairwise comparisons differentiated 95.6% with potassium as the elemental ratio, showing that the discrimination capabilities of LIBS are comparable to those of LA-ICP-MS. Due to the many advantages of LIBS, this instrument should be considered a necessity for forensic laboratories.

Determination of Signature Volatile Odor Chemicals Emanating from Novel Biological Specimens by Non-invasive Analytical Techniques for the Potential use in Forensic Identifications

Human scent, or the volatile organic compounds (VOCs) produced by an individual, has been recognized as a biometric measurement because of the distinct variations in both the presence and abundance of these VOCs between individuals. In forensic science, human scent has been used as a form of associative evidence by linking a suspect to a scene/object through the use of human scent discriminating canines. The scent most often collected and used with these specially trained canines is from the hands because a majority of the evidence collected is likely to have been handled by the suspect. However, the scents from other biological specimens, especially those that are likely to be present at scenes of violent crimes, have yet to be explored. Hair, fingernails and saliva are examples of these types of specimens. In this work, a headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) technique was used for the identification of VOCs from hand odor, hair, fingernails and saliva. Sixty individuals were sampled and the profiles of the extracted VOCs were evaluated to assess whether they could be used for distinguishing individuals. Preliminary analysis of the biological specimens collected from an individual (intra-subject) showed that, though these materials have some VOCs in common, their overall chemical profile is different for each specimen type. Pair-wise comparisons, using Spearman Rank correlations, were made between the chemical profiles obtained from each subject, per a specimen type. Greater than 98.8% of the collected samples were distinguished from the subjects for all of the specimen types, demonstrating that these specimens can be used for distinguishing individuals. Additionally, field trials were performed to determine the utility of these specimens as scent sources for human scent discriminating canines. Three trials were conducted to evaluate hair, fingernails and saliva in comparison to hand odor, which was considered the standard source of human odor. It was revealed that canines perform similarly to these alternative human scent sources as they do to hand odor implying that, though there are differences in the chemical profiles released by these specimens, they can still be used for the discrimination of individuals by trained canines.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.