The majority of daily living tasks necessitate the use of bimanual movements or concurrent cognitive processing, which are often more difficult for elderly adults. With the number of Americans age 65 and older expected to double in the next 25 years, in-depth research and sophisticated technologies are necessary to understand the mechanisms involved in normal neuromuscular aging. The objective of the research is to understand the effects of aging on biological signals for motor control and to develop a methodology to classify aging and stroke populations. The methodological approach investigated the influence on correlated activity (coherence) between electroencephalogram (EEG) and electromyogram (EMG) signals into senior age. In support of classifying aging and stroke populations, the methodology selected optimal features from the time, frequency, and information theory domains. Additionally, the use of cepstral analysis was modified toward this application to analyze EEG and EMG signals. The inclusion and optimization of cepstral features significantly improved classification accuracy. Additionally, classification of young and elderly adults using Gaussian Mixture Models with Minimum Classification Error improved overall accuracy values. Contributions from the dissertation include demonstration of the change in correlated activity between EMG and EEG with fine motor simple and complex dual tasks; a quantitative feature library for characterizing the neural control of movement with aging under three task conditions; and a methodology for the selection and classification of features to characterize the neural control of movement. Additionally, the dissertation provides functional insight for the association of features with tasks, aging, and clinical conditions. The results of the work are significant because classification of the neural control of movement with aging is not well established. From these contributions, future potential contributions are: a methodology for physiologists to analyze and interpret data; and a computational tool to provide early detection of neuromuscular disorders.

No description available.

In this thesis, we develop and test a technology for computer-assisted assessments of newborn brain maturity from sleep electroencephalogram (EEG). Brain maturation of newborns is reflected in rapid development of EEG patterns over a number of weeks after conception. Observing the maturational patterns, experts can assess newborn’s EEG maturity with an accuracy ±2 weeks of newborn’s stated age. A mismatch between the EEG patterns and newborn’s physiological age alerts clinicians about possible neurological problems. Analysis of newborn EEG requires specialised skills to recognise the maturity-related waveforms and patterns and interpret them in the context of newborns age and behavioural state. It is highly desirable to make the results of maturity assessment most accurate and reliable. However, the expert analysis is limited in capability to estimate the uncertainty in assessments. To enable experts quantitatively evaluate risks of brain dysmaturity for each case, we employ the Bayesian model averaging methodology. This methodology, in theory, provides the most accurate assessments along with the estimates of uncertainty, enabling experts to take into account the full information about the risk of decision making. Such information is particularly important when assessing the EEG signals which are highly variable and corrupted by artefacts. The use of decision tree models within the Bayesian averaging enables interpreting the results as a set of rules and finding the EEG features which make the most important contribution to assessments. The developed technology was tested on approximately 1,000 EEG recordings of newborns aged 36 to 45 weeks post conception, and the accuracy of assessments was comparable to that achieved by EEG experts. In addition, it was shown that the Bayesian assessment can be used to quantitatively evaluate the risk of brain dysmaturity for each EEG recording.

Brain-Computer Interface (BCI) systems aim to provide a non-muscular channel for the brain to control external devices using electrical activities of the brain. These BCI systems can be used in various applications, such as controlling a wheelchair, neuroprosthesis, or speech synthesizer for disabled individuals, navigation in virtual environment, and assisting healthy individuals in performing highly demanding tasks. Motor-imagery BCI systems in particular are based on decoding imagination of motor tasks, e.g., to control the movement of a wheelchair or a mouse curser on the computer screen and move it to the right or left directions by imagining right/left hand movement. During the past decade, there has been a growing interest in utilization of electroencephalogram (EEG) signals for non-invasive motor-imagery BCI systems, due to their low cost, ease of use, and widespread availability. During motor-imagery tasks, multichannel EEG signals exhibit task-specific features in both spatial domain and spectral (or frequency) domain. This thesis studies the statistical characteristics of the multichannel EEG signals in these two domains and proposes a new approach for spatio-spectral feature extraction in motor-imagery BCI systems. This approach is based on the fact that due to the multichannel structure of the EEG data, its spatio-spectral features have a matrix-variate structure. This structure, which has been overlooked in the literate, can be exploited to design more efficient feature extraction methods for motor-imagery BCIs. Towards this end, this research work adopts a matrix-variate Gaussian model for the spatio-spectral features, which assumes a separable Kronecker product structure for the covariance of these features. This separable structure, together with the general properties of the Gaussian model, enables us to design new feature extraction schemes which can operate on the data in its inherent matrix-variate structure to reduce the computational cost of the BCI system while improving its performance. Throughout this thesis, the proposed matrix-variate model and its implications will be studied in various different feature extraction scenarios.

Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP) slope, were assessed in predicating outcome to chronic treatment with one of three antidepressant regimens [escitalopram (ESC); bupropion (BUP); ESC+BUP]. Relative to controls, depressed adults had lower pretreatment cortical activity in regions implicated in approach motives/positive processing. Increased anterior cingulate cortex (ACC)-localized theta was observed, possibly reflecting emotion/cognitive regulation disturbances in the disorder. AEPs and LDAEPs, putative indices of serotonin activity (implicated in MDD etiology), were largely unaltered in MDD. Assessment of ERPs to facial expression processing indicated slightly blunted late preconscious perceptual processing of expressions, and prolonged processing of intensely sad faces in MDD. Faces were rated as sadder overall in MDD, indicating a negative processing bias. Treatment responders (vs. non-responders) exhibited baseline cortical hypoactivity; after a week of treatment, cortical arousal emerged in responders. Increased baseline left fronto-cortical activity and early shifts towards this profile were noted in responders (vs. non-responders). Responders exhibited a steep, and non-responders shallow, baseline N1 LDAEP derived from primary auditory cortex activity. P2 LDAEP slopes (primary auditory cortex-derived) increased after a week of treatment in responders and decreased in non-responders. Consistent with overall findings, ESC responders displayed baseline cortical hypoactivity and steep LDAEP-sLORETA slopes (vs. non-responders). BUP responders also exhibited steep baseline slopes and high ACC theta. These results indicate that specific resting brain activity profiles appear to distinguish depressed from non-depressed individuals. Subtle ERP modulations to simple auditory and emotive processing also existed in MDD. Resting alpha power, ACC theta activity and LDAEP slopes predicted antidepressant response in general, but were limited in predicting outcome to a particular treatment, which may be associated with limited sample sizes.

SUMMARY Introduction: There has recently been a great deal of interest in the role of synchronous high-frequency gamma oscillations in brain function. This interest has been motivated by an increasing body of evidence, that oscillations which are synchronous in phase across separated neuronal populations, may represent an important mechanism by which the brain binds or integrates spatially distributed processing activity which is related to the same object. Many models of schizophrenia suggest an impairment in the integration of brain processing, such as a loosening of associations, disconnection, defective multiple constraint organization, or cognitive dysmetria. This has led to recent speculation that abnormalities of high-frequency gamma synchronization may reflect a core dimension of the disturbance underlying this disorder. However, examination of the phase synchronization of gamma oscillations in patients with schizophrenia has never been previously undertaken. Method: In this thesis a new method of analysis of gamma synchrony was introduced, which enables the phase relationships of oscillations in a specific frequency band to be examined across multiple scalp sites as a function of time. This enabled, for the first time, the phase synchronization of gamma oscillations across widespread regions, to be studied in electrical brain activity measured at the scalp in humans. Gamma synchrony responses were studied in electroencephalographic (EEG) data acquired during a commonly employed conventional auditory oddball paradigm. The research consisted of two sets of experiments. In the first set of experiments, data from 100 normal subjects, consisting of 10 males and 10 females in each age decade from 20 to 70, was examined. These experiments were designed to characterize the gamma synchonizations that occurred in response to target and background stimuli and their functional significance in normal brain activity, and to exclude the possibility of these findings being due to electromyogram (EMG) or volume conduction artifact. The examination of functional significance involved the development of an additional new analysis technique. In the second set of experiments, data acquired from 35 patients with schizophrenia and 35 matched normal controls was analyzed. The purpose of these experiments was to determine whether patients showed disturbances of gamma synchrony compared to controls, and to establish the relationship of any such disturbances to medication levels, symptom profiles, duration of illness, and a range of psychophysiological variables. Results: In the 100 normals, responses to target stimuli were characterized by two bursts of synchronous gamma oscillations, an early (evoked) and a late (induced) synchronization, with different topographic distributions. Only the early gamma synchronization was seen in response to background stimuli. The main variable modulating the magnitude of these gamma synchronizations from epoch to epoch was pre-stimulus EEG theta (3-7 Hz) and delta (1-3 Hz) power. Early and late gamma synchrony were also associated with N1 and P3 ERP component amplitude across epochs. Across subjects, the early gamma synchronization was associated with shorter latency of the ERP components P2, N2 and P3, smaller amplitude of N1 and P2, and smaller pre-stimulus beta power. The control analyses showed that these gamma responses were specific to a narrow frequency range (37 to 41 Hz), and were not present in adjacent frequency bands. The responses were not generated by EMG contamination or volume conduction. In the 35 patients with schizophrenia, significant abnormalities of both the early and late synchronizations were observed compared to the 35 normal controls, with distinctive topographic characteristics. In general, early gamma synchrony was increased in patients compared to controls, and late gamma synchrony was decreased. These gamma synchrony disturbances were not related to medication level or the four summed symptom profile scores (positive, negative, general and total). They were, however, associated with duration of illness, becoming less severe the longer the patient had suffered from the disorder. The disordered gamma synchrony in patients was not secondary to abnormalities in other psychophysiological variables, but appeared to represent a primary disturbance. Discussion: The early synchronization may relate to the binding of object representations in early sensory processing, or, given that a constant inter-stimulus interval was employed, may be anticipatory and related to active memory. The late response is probably involved in binding in relation to activation of the internal contextual model involved in late expectancy/contextual processing (context updating or context closure) for target stimuli. The across epochs effects may relate to whether the focus of attention immediately prior to stimulus presentation is internal or is directed at the task. The across subjects effects suggest that a larger magnitude of the early gamma synchronization might indicate that the subject maintains a more stable and less ambiguous internal representation of the environment, that reduces the complexity of input and facilitates target/background discrimination and subsequent processing. The early gamma synchronization findings in patients with schizophrenia suggest that anticipatory processing involving active memory and forward-prediction of the environment is subject to over-binding or the formation of inappropriate associations. The late synchronization disturbances may reflect a fragmentation of contextual processing, and an inability to maintain contextual models of the environment intact over time. Conclusion: This research demonstrates the potential importance of integrative network activity as indexed by gamma phase synchrony in relation to normal cognition, and the possible broad relevance of such activity in psychiatric disorders. In particular, the application in this study to patients with schizophrenia showed that an impairment of brain integrative activity (missing links) might be a key feature of this illness.

Σκοπός της παρούσας διπλωματικής εργασίας είναι η μελέτη του συγχρονισμού των ηλεκτροεγκεφαλικών σημάτων ως μέθοδος διάγνωσης των μαθησιακών δυσκολιών. Τα δεδομένα που χρησιμοποιήθηκαν είναι ηλεκτροεγκεφαλογραφήματα ηρεμίας και εγκεφαλικά προκλητικά δυναμικά υγιών και ατόμων με μαθησιακές δυσκολίες. Η μεθοδολογία που εξετάζεται βασίζεται στον υπολογισμό γραμμικών και μη γραμμικών μεγεθών. Τα γραμμικά μεγέθη περιλαμβάνουν την ετεροσυσχέτιση (cross-correlation) και τη συνοχή (coherence). Τα μη γραμμικά μεγέθη περιλαμβάνουν πέντε μεγέθη που προκύπτουν από την ανακατασκευή του σήματος στο χώρο των καταστάσεων, και δύο μεγέθη που προκύπτουν από το μετασχηματισμό Hilbert. Τα αποτελέσματα της ανάλυσης δείχνουν ότι ο συγχρονισμός στα άτομα με μαθησιακές δυσκολίες είναι σημαντικά ελαττωμένος σε σχέση με τους υγιείς στο σύνολο των ηλεκτροδίων και των εγκεφαλικών ρυθμών. / The aim of the present thesis is the analysis of the synchronization of the electroencephalogram as a method for the diagnosis of learning difficulties. The utilized data are electroencephalogram and evoked potentials of healthy subjects and subjects with learning difficulties. The method includes the estimation of linear and non linear measures. Two linear measures are used, namely cross-correlation and coherence. Five non linear measures are based on the reconstruction of the state space, and two non linear measures are based on the Hilbert transform. Driver synchronization is observed in subjects with learning difficulties as demonstrated by all the studied measures.

Το σύμπλεγμα-Κ και η άτρακτος είναι από τα χαρακτηριστικότερα ηλεκτροεγκεφαλογραφικά στοιχεία του δευτέρου NREM σταδίου του ύπνου στον άνθρωπο. Η παρούσα μελέτη διεξήχθη με σκοπό να διερευνήσει πιθανές σχέσεις μεταξύ των φαινομένων αυτών που εμφανίζονται κατά το δεύτερο στάδιο του NREM ύπνου, με ταυτόχρονη φιλοδοξία να απαντήσει σε ερωτήματα σχετικά με τον λειτουργικό ρόλο αυτών. Δέκα υγιή υποκείμενα έλαβαν μέρος στην μελέτη, κατά την οποία ελήφθη το ολονύχτιο ηλεκτροεγκεφαλογράφημα του ύπνου τους. Τα φαινόμενα ενδιεφέροντος αναγνωρίστηκαν, επιλέχθησαν και σημειώθηκαν προκειμένου να υποβληθούν σε συμβαντο-σχετιζόμενη ανάλυση, η οποία ενισχύθηκε από την εφαρμογή ανάλυσης χρόνου-συχνοτήτων καθώς και διδιάστατης τοπογραφικής απεικόνισης στον χώρο των ηλεκτροδίων. Η παρούσα μελέτη οδήγησε στα παρακάτω ευρήματα σχετικά με τα κύρια ηλεκτροεγκεφαλογραφικά στοιχεία του δευτέρου NREM σταδίου του ανθρώπινου ύπνου. Ταχείες άτρακτοι στην πορεία των οποίων τυγχάνει να εμφανιστεί σύμπλεγμα-Κ διακόπτουν την ταλάντωσή τους. Άτρακτοι οι οποίες με μεγάλη πιθανότητα εμφανίζονται μετά την πάροδο του συμπλέγματος-Κ έχουν υψηλότερη συχνότητα ταλάντωσης τόσο από εκείνες που διεκόπησαν όσο και από τις σποραδικές, μη-συσχετισμένες με σύμπλεγμα-Κ, ατράκτους. Η εν λόγω αύξηση στην συχνότητα τείνει προς μια μέγιστη συχνότητα ταλάντωσης και δεν εξαρτάται από κάποιο από τα ηλεκτροεγκεφαλογραφικά καταγραφόμενα χαρακτηριστικά του συμπλέγματος-Κ. Αντίστοιχα, και η διακοπή της ταλάντωσης των ατράκτων πρό του συμπλέγματος-Κ δεν μπορεί με βεβαιότητα να αποδωθεί στο σύμπλεγμα-Κ. Εντός του συμπλέγματος-Κ εμφανίζεται με μεγάλη πιθανότητα ρυθμική δραστηριότητα στο άνω όριο της θήτα ζώνης συχνοτήτων. Η δραστηριότητα αυτή είναι ανεξάρτητη του συμπλέγματος-Κ, καθώς εμφανίζει κατά πλειονότητα προσθιο-οπίσθιο προφίλ διάδοσης όταν το αργό κύμα του συμπλέγματος-Κ έχει μετωπιαία εντόπιση. Ο υψηλός θήτα ρυθμός αυτός, όσο περισσότερες ταλαντώσεις επιτυγχάνει εντός του συμπλέγματος-Κ τόσο περισσότερο τείνει να εισέλθει συχνοτικά στην άλφα ζώνη συχνοτήτων, από την οποία κατά πλειονότητα επανέρχεται στα αρχικά συχνοτικά επίπεδα κατά την τελευταία ταλάντωση˙ μια φασματική επαναφορά προτύπου πέδησης, την οποία ελλείψει άλλων οπτικά αξιολογήσιμων στοιχείων αποδίδουμε στην ύστερη δράση του συμπλέγματος-Κ. Η μελέτη αυτή έδειξε: α) ότι το σύμπλεγμα-Κ και οι άτρακτοι αποτελούν υπναγωγικά στοιχεία του φλοιού και του θαλάμου αντίστοιχα, τα οποία αντικατοπτρίζουν ανεξάρτητες αλλά συνεργές διαδικασίες που σκοπό έχουν να διατηρήσουν την συνέχεια του ύπνου, και β) ότι οι αλληλεπιδράσεις του συμπλέγματος-Κ με τους περί αυτό ρυθμούς υποδεικνύουν την ύπαρξη μιας ακόμη πιο βραχύχρονης δυναμικής διεργασίας στην μικρο-αρχιτεκτονική του ύπνου. Παράλληλα η μελέτη οδήγησε στην ανάπτυξη ενός νέου εργαλείου ταυτόχρονης απεικόνισης της μακρο-αρχιτεκτονικής και της μικρο-αρχιτεκτονικής του ύπνου: του υπνοφασματογραφήματος, του διαγράμματος χρόνου-συχνοτήτων του ολονύχτιου ηλεκτροεγκεφαλογραφήματος του ύπνου. / The K-complex and the sleep spindle are the most distinctive electroencephalographic features of the the second stage of NREM human sleep. This study was performed in order to investigate the potential relations between these phenomena that make their appearance in the second stage of NREM sleep, at the same time aiming in answering questions regarding their physiological role. Ten healthy individuals participated in this study, during which the whole-night sleep electroencephalogram was derived. The features of interest were identified, selected and marked in order to undergo event-related analysis, empowered by time-frequency analysis and 2-dimentional topography in electrode space. This study led to the following conclusions regarding the main electroencephalographic features of the second stage of NREM human sleep. Fast sleep spindles, during the course of which a K-complex happens to appear, interrupt their oscillation. The spindles that appear with high probability after the K-complex have higher frequency of oscillation from both the interrupted ones as well as form the sporadic, non-cprrelated to the K-complex, spindles. This increment in frequency tends towards a maximum oscillation frequency and is not dependent in any of the electroencephalographically recorded features of the K-complex. Respectively, the interruption of the oscillation of the pre-K-complex spindles cannot be credited to the K-complex with certainty. During the course of the K-complex a rhythmic activity of the upper theta band appears with high probability. This activity is independent from the K-complex as, in majority, it exhibits a antero-posterior profile of displacement when the slow wave of the K-complex has a frontal localization. This high-theta rhythm, the more oscillation peaks it achieves during the K-complex, the more it tends to enter the alpha band, from which, in majority, it returns to the initial frequency levels during the last oscillation; a spectral breaking profile that, in the absence of other visually evaluated elements, we credit to the late action of the K-complex. This study showed that: a) the K-complex and the sleep spindle are sleep-promoting elements of the cortex and the thalamus, respectively, that represent independent but cooperative processes that aim in preserving the continuity of sleep, and b) that the interactions of the K-complex with the rhythms around it reveal the existence of an even briefer dynamic process of the micro-architecture of sleep. At the same time, this study led to the development of a novel tool for concurrent visualization of the macro-architecture and the micro-architecture of sleep: the hypnospectrogram, the time-frequency plot of the whole-night sleep electroencephalogram.

Σκοπός της παρούσας μεταπτυχιακής διπλωματικής εργασίας είναι η μελέτη και σχεδίαση ενός υδατογραφικού σχήματος για ηλεκτροεγκεφαλογραφικά (ΗΕΓ) σήματα επιληπτικών ασθενών. Το υδατογραφικό σχήμα που εφαρμόστηκε βασίζεται στο Μετασχηματισμό Κυματιδίων (wavelet transform) και είχε μέχρι πρότινος εφαρμοστεί μόνο σε ιατρικές εικόνες. Στόχος της ανάπτυξης του υδατογραφικού αυτού σχήματος, είναι η ενσωμάτωση πληροφοριών που έχουν μεγάλη αξία για διάγνωση και θεραπεία χωρίς όμως να αλλοιώνεται αισθητά το σήμα μετά την ενσωμάτωση των δεδομένων. Πριν την εφαρμογή όμως του υδατογραφικού μας σχήματος, απομονώνεται με έναν ειδικά σχεδιασμένο αλγόριθμο η περιοχή της επιληπτικής κρίσης γιατί είναι η περιοχή με τη μεγαλύτερη διαγνωστική αξία και στόχος είναι να παραμείνει εντελώς αναλλοίωτη. Η πρόοδος στον τομέα της τηλεϊατρικής έχει επιτρέψει τη μεταφορά ιατρικών σημάτων με στόχο τη διάγνωση και θεραπεία ασθενών που βρίσκονται σε απομακρυσμένες περιοχές. Κατά τη μεταφορά του υδατογραφημένου σήματος όμως μπορεί αυτό να υποστεί αλλοιώσεις που προέρχονται από συμπιέσεις του σήματος με σκοπό τη μείωση του μεγέθους τους, αλλά και αλλοιώσεις που προέρχονται από προσθήκη θορύβου. Για να εκτιμηθεί η αποτελεσματικότητα λοιπόν του υδατογραφικού μας σχήματος, εφαρμόζονται στο σήμα επιθέσεις συμπίεσης με διαφορετικά κατώφλια και επιθέσεις προσθήκης λευκού θορύβου με διαφορετικούς σηματοθορυβικούς λόγους SNR. Εφαρμόζοντας αυτές τις επιθέσεις, υπολογίζεται το ποσοστό ανάκτησης του υδατογραφήματος από το σήμα που έχει υποστεί επίθεση καθώς και το ποσοστό αλλοίωσης μεταξύ του αρχικού υδατογραφημένου σήματος και του υδατογραφημένου σήματος που έχει υποστεί επίθεση. Μεγαλύτερο ποσοστό ανάκτησης του υδατογραφήματος παρατηρείται όσο το κατώφλι συμπίεσης μικραίνει ενώ αντίθετα ο σηματοθορυβικός λόγος SNR μεγαλώνει. / The purpose of this thesis is the disquisition of a digital watermarking scheme for electroencephalogram (EEG) signals designed for epileptic patients. The watermarking scheme that has been applied in EEG signals is based on wavelet transform applied only in medical images. The objective implementing this digital watermarking scheme in EEG signals, is to embed important data with great significance in patient’s medical history. Furthermore, the scheme can be used for: diagnosis and cure, without distort the initial signal in such a way that leads in a misdiagnosis. Prior to implementation of our watermarking scheme, the area that presents the epileptic seizure is isolated with a specific designed algorithm so as the signal in this area remains undistorted. Nowadays, modern telecommunication infrastructure supports the possibility of delivering quality health care without the physical presence of medical experts. During the telecommunication signal transfers, the watermarking signals can be distorted due to compression methods or/and addition of white noise. In order to evaluate the efficiency of watermarking scheme, the signal is subjected to different kinds of attacks, such as compression with different compression thresholds, and attacks of adding white noise with different SNR ratio. After applying these attacks to the signal, it is computed the recovery ratio of the watermark and the distortion between the initial watermarked signal and the signal that has been subjected to the attacks. Given the results, the conclusion is that the smaller the compression threshold is, the better the recovery ratio of the watermark. On the other hand, in white noise attacks, the recovery ratio increases as the SNR ratio gets higher.

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.