Dissertations / Theses on the topic 'Electroencephalography'

This master thesis report describes signal processing parameters of electroencephalography (EEG) signals with a significant difference between the signals from the animal model of clinical depression and the non-depressed animal model. The signal from the depressed model had a weaker power in gamma (30 - 80 Hz) than the non-depressed model during awake and it had a stronger power in delta (1.5 - 4 Hz) during sleep. The report describes the process of using visualisation to understand the shape of the signal which helps with interpreting results and helps with the development of parameters. A generic tool for time-frequency analysis was improved to cope with the size of the weeklong EEG dataset. A method for evaluating the quality of how well the EEG parameters are able to separate the strains with as short recordings as possible was developed. This project shows that it is possible to separate an animal model of depression from an animal model of non-depression based on its EEG and that EEG-classifiers may work as indicative classifiers for depression. Not a lot of data is needed. Further studies are needed to verify that the results are not overly sensitive to recording setup and to study to what extent the results are translational. It might be some of the EEG parameters with significant differences described here are limited to describe the difference between the two strains FSL and SD. But the classifiers have reasonable biological explanations that makes them good candidates for being translational EEG-based classifiers for clinical depression.

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand's movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand's motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy.

This report is about a project where electroencephalography (EEG) wasused to control a two player game. The signals from the EEG-electrodeswere amplified, filtered and processed. Then the signals from the playerswere compared and an algorithm decided what would happen in the gamedepending on which signal was largest. The controls and the gaming mechanismworked as intended, however it was not possible to gather a signal fromthe brain with the method used in this project. So ultimately the goal wasnot reached.
electroencephalography, EEG

Thesis (M Med (Psychiatry))--University of Limpopo (Medunsa Campus), 2010.
The yield of EEG amongst psychiatric patients has been reported to be low and the value of EEG in the practice of psychiatry is questionable.EEG is used as part of a diagnostic work up for patients with psychiatric disorders .Often the reason given for its use is to exclude epilepsy as a cause of psychiatric symptoms. Epilepsy is primarily a clinical diagnosis, but the EEG may provide strong support by the findings of inter – ictal Epileptogenic discharge METHOD: All the adult EEGs requested at Dr George Mukhari psychiatric hospital, over a 36 month period ,were reviewed to describe the outcome of the requested EEG reports .The study is a simple retrospective analysis of 111 consecutive EEG requested to the department of Neurology at DGMH from psychiatric unit at DGMH. Subjects were both inpatients and outpatients .All the EEG was reported by a qualified Neurologist. Data were extracted from the EEG request form and the patients’ clinical files, which reported on the clinical reason for the EEG test, nature of psychiatric diagnosis of patients, the psychiatric treatment received prior to the EEG test and the nature of the EEG results RESULTS There were 111 EEG reports analysed, and 69 EEG reports for males and 42 EEG reports for females. The reason for EEG request was dominated mainly by exclusion of epilepsy. Majority of the patients were diagnosed with a psychotic disorder , followed second by a mood disorder , all of which was attributed to GMC (epilepsy).About 62.73% of patients were on a combination of treatment of antipsychotic drug and anticonvulsants, whilst 34.55% were on antipsychotic monotherapy prior to the EEG test. Further analysis of the requested EEG form was carried out in whom the test was to determine whether or not the patients were suffering from epilepsy.EEG abnormalities were identified amongst 24% of the patients. About 11,7% of patients presented with non specific EEG results .Out of a total number of 111 patients whom an EEG test was requested and epilepsy was highly suspected from clinical presentation, only 14 patients (12.6%),presented with epileptiform discharge on their EEG results. However majority of the patients (76%) demonstrated normal EEG pattern, which doesn’t exclude a diagnosis of epilepsy. CONCLUSION The yield of EEG in psychiatry is low. To diagnose epilepsy as a cause of psychiatric presentation, clinicians should continue to rely on the clinical history of attacks and not the EEG .In the practice of psychiatry it is not recommended to routinely order an EEG to exclude a diagnosis of epilepsy, more so to confirm a psychiatric diagnosis.The presence of a psychiatric symptoms in patients who presents with epilepsy, is rarely associated with meaningful EEG changes

Recent years have seen an explosion of user-generated, untagged multimedia data, generating a need for efficient search and retrieval of this data. The predominant method for content-based tagging is through manual annotation. Consequently, automatic tagging is currently the subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users' responses to the multimedia content are analysed in order to generate descriptive tags. We approach this problem through the modalities of facial expressions and EEG signals. We investigate tag validation and affective tagging using EEG signals. The former relies on the detection of event-related potentials triggered in response to the presentation of invalid tags alongside multimedia material. We demonstrate significant differences in users' EEG responses for valid versus invalid tags, and present results towards single-trial classification. For affective tagging, we propose methodologies to map EEG signals onto the valence-arousal space and perform both binary classification as well as regression into this space. We apply these methods in a real-time affective recommendation system. We also investigate the analysis of facial expressions for implicit tagging. This relies on a dynamic texture representation using non-rigid registration that we first evaluate on the problem of facial action unit recognition. We present results on well-known datasets (with both posed and spontaneous expressions) comparable to the state of the art in the field. Finally, we present a multi-modal approach that fuses both modalities for affective tagging. We perform classification in the valence-arousal space based on these modalities and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information.

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.

The optimal performance of an action depends to a great extend on the ability of a person to prepare in advance the appropriate kinetic and kinematic parameters at a specific point in time in order to meet the demands of a given situation and to foresee its consequences to the surrounding environment. In the research presented in this thesis, I employed high-density electroencephalography in order to study the neural processes underlying preparation for action. A typical way for studying preparation for action in neuroscience is to divide it in temporal preparation (when to respond) and event preparation (what response to make). In Chapter 2, we identified electrophysiological signs of implicit temporal preparation in a task where such preparation was not essential for the performance of the task. Electrophysiological traces of implicit timing were found in lateral premotor, parietal as well as occipital cortices. In Chapter 3, explicit temporal preparation was assessed by comparing anticipatory and reactive responses to periodically or randomly applied external loads, respectively. Higher (pre)motor preparatory activity was recorded in the former case, which resulted in lower post-load motor cortex activation and consequently to lower long-latency reflex amplitude. Event preparation was the theme of Chapter 4, where we introduced a new method for studying (at the source level) the generator mechanisms of lateralized potentials related to response selection, through the interaction with steady-state somatosensory responses. Finally, in Chapter 5 we provided evidence for the existence of concurrent and mutually inhibiting representations of multiple movement options in premotor and primary motor areas.

The heritability of frontal EEG asymmetry, a potential endophenotype for depression, was investigated using a large set of adolescent and young adult twins. Additionally, the relationship between polymorphisms within three serotonin genes, two receptor genes and one transporter gene, and frontal EEG asymmetry was also investigated. Using Falconer's estimate, frontal EEG asymmetry was shown to be more heritable at lateral compared to medial cites across nearly all reference montages, and greater in males compared to females. Using structural equation modeling (SEM), and investigating both additive (ACE) and non-additive (ADE) models of genetic heritability, males displayed consistently greater additive genetic contributions to heritability, with greater lateral contributions than medial ones. For female twins pairs, the additive genetic model data provided a mixed picture, with more consistent heritability estimates observed at medial sites, but with larger estimates shown at lateral channels. For non-additive genetic models, male twin pairs demonstrated exclusive non-additive contributions to heritability across channels within AVG and CZ referenced data, with metrics in the CSD and LM montages more mixed between additive and non-additive contributions. However, consistent with Falconer's estimates, lateral channels were nearly always estimated to be more heritable than medial channels regardless of gender. These models demonstrate some combination of additive and non-additive contributions to the heritability of frontal EEG asymmetry, with the CSD and AVG montages showing greater lateral compared to medial heritability and CZ and LM montages showing mixed contributions with additive heritability at lateral channels and non-additive primarily at medial channels. The complex interaction of gender and reference montage on the heritability estimates highlight the subtle yet important roles of age, gender, and recording methodology when investigating proposed endophenotypes. However, no association was found between the proposed polymorphisms in serotonin receptor 1a, 2a or serotonin transporter genes and frontal EEG asymmetry. Although the results support modest heritability of frontal EEG asymmetry, the proposed link to underlying serotonergic genetic markers remains an open question. Overall, these results indicate that frontal asymmetry may be a useful endophenotype for depressive risk with modest heritability, but is one that taps more environmental risk.

Human movement requires adequate postural control. Stimulation of the sensory systems induces alterations in body sway. However, the role of cortical activity in maintaining balance remains unclear. The purpose of this research was to extend the understanding of cortical involvement in human postural control and provide direct and indirect cortical evidences from the visual system and postural demand. Ultimately, this research provides critical insight into the mechanisms of adaptive and maladaptive postural control.

The ongoing electrical activity of the brain is known as the electroencephalogram (EEG). Evoked potentials (EPs) are voltage deviations in the EEG elicited in association with stimuli. EPs provide clinical information by allowing an insight into neurological processes. The amplitude of EPs is typically several times less than the background EEG. The background EEG has the effect of obscuring the EPs and therefore appropriate signal processing is required for their recovery. The EEG waveforms recorded from electrodes placed on the scalp contains the ongoing background EEG, EPs from various brain sources as well as signal components with sources external to the brain. An example of externally generated signal which is picked up by the electrodes on the scalp is the electrooculogram (EOG). This signal is generated by the eyes when eye movements or blinks are performed. Saccade-related EEG waveforms were recorded from 7 normal subjects. A signal source separation technique, namely the independent component analysis (ICA) algorithm of Bell and Sejnowski (hereafter refereed to as BS_ICA), was employed to analyse the recorded waveforms. The effectiveness of the BS_ICA algorithm as well as that of the ICA algorithm of Cardoso, was investigated for removing ocular artefact (OA) from the EEG. It was quantitavely demonstrated that both ICA algorithms were more effective than the conventional correlation-based techniques for removing the OA from the EEG.A novel iterative synchronised averaging method for EPs was devised. The method optimally synchronised the waveforms from successive trials with respect to the event of interest prior to averaging and thus preserved the features of the signals components that were time-locked to the event. The recorded EEG waveforms were analysed using BS_ICA and saccade-related components (frontal and occipital pre-saccadic potentials, and the lambda wave) were extracted and their scalp topographies were obtained. This initial study highlighted some limitations of the conventional ICA approach of Bell and Sejnowski for analysing saccade-related EEG waveforms. Novel techniques were devised in order to improve the performance of the ICA algorithm of Bell and Sejnowski for extracting the lambda wave EP component. One approach involved designing a template-model that represented the temporal characteristics of a lambda wave. Its incorporation into the BS_ICA algorithm improved the signal source separation ability of the algorithm for extracting the lambda wave from the EEG waveforms. The second approach increased the effective length of the recorded EEG traces prior to their processing by the BS_ICA algorithm. This involved abutting EEG traces from an appropriate number of successive trials (a trial was a set of waveforms recorded from 64 electrode locations in a experiment involving a saccade performance). It was quantitatively demonstrated that the process of abutting EEG waveforms was a valuable pre-processing operation for the ICA algorithm of Bell and Sejnowski when extracting the lambda wave. A Fuzzy logic method was implemented to identify BS_ICA-extracted single-trial saccade-related lambda waves. The method provided an effective means to automate the identification of the lambda waves extracted by BS_ICA. The approach correctly identified the single-trial lambda waves with an Accuracy of 97.4%.

Diagnosing post-traumatic stress disorder (PTSD) is challenging and is currently, diagnosis through self-administered checklists. Because a diagnosis of PTSD can open up significant benefits to compensation, education, and medical care, people can tailor their responses to the checklist to help ensure a diagnosis of PTSD. The purpose of the study was to examine the utility of the quantitative electroencephalograph for diagnosing PTSD. Frequency and presence of biomarkers and alpha brain wave symmetry in the frontal and parietal lobes were examined. Research questions involved examining the presence of alpha wave imbalance across the frontal lobe and between the right and left parietal lobes. A secondary data analysis was conducted using data from 108 subjects; these data included records from those with and without a PTSD diagnosis. The results of logistic regression showed that 63% of the clients diagnosed with PTSD were correctly identified and between 7% and 8% of the variance in PTSD was accounted for by frontal lobe asymmetry. The parietal lobe imbalance correctly classified PTSD in 59% of the patients and it identified 3.5–4.9% of the variance, suggesting that asymmetry in the frontal and parietal lobes should not be used as the primary method for diagnosing PTSD. Implications for social change include identifying an objective diagnostic tool that can potentially decrease the possibility of inaccurate diagnoses based on self-reported symptoms. This could lead to eliminating some of the shame and embarrassment veterans and first responders feel toward seeking help for PTSD.

This study uses statistical techniques to determine anesthetic depths of three females undergoing total abdominal hysterectomies. Spectral analysis of the electronencephalogram is employed to define changes in brain wave activity under different levels of anesthesia after administration of diazepam and isoflurane. The multivariate statistical technique of discriminant function analysis is used to determine which frequencies, or linear combinations of frequencies, yield the most information for classification of the electronencephalogram samples into one of the three anesthetic depths (mild sedation, moderate anesthesia, and anesthetic sleep). Spectral analysis of the electronencephalogram showed similar results for all three patients after administration of diazepam (mild sedation), but widely varying results among patients during anesthesia using isoflurane. The combination of spectral analysis and discriminant function analysis showed reliable discrimination among the three anesthetic depths. The ability to discriminate was significantly improved when only two anesthetic depths were used.

Event-related potentials (ERPs) were evoked by visually presented words in a number of experimental paradigms. The question of which linguistic factors, if any, underlie differences between visual word ERPs was addressed. These studies identified 3 factors as predictors of ERF variance. Studies of ERPs in language processing tasks are selectively reviewed, and methodological problems associated with ERPs evoked by non-identical stimuli are discussed. The importance of an understanding of the linguistic factors which underlie ERP differences is outlined, and a methodology for approaching this issue is set out. The statistical procedure necessary to address the question is developed and described in Chapter Two. This procedure was a quantitative modelling strategy, based on multidimensional scaling and PROCRUSTES rotation. Five quantitative modelling studies were undertaken. These experiments involved two experimental tasks, a passive exposure task in which the subjects attended but did not respond to the stimuli (experiment 1) and a category membership decision task (experiments 2 to 5). Words drawn from two semantic categories were employed. ERPs were evoked by individual members of the category of colour names (experiments 1 to 3) and by members of the category of furniture terms (experiments 4 and 5). The results of these studies suggested that word length was the important factor in the early part of the post-stimulus epoch and that this factor was followed by semantic similarity. A late positivity was present in the decision task ERPs whose modulation was related to word frequency. These results were validated by two conventionally analysed experiments which examined the relation between word length and repetition and that between word frequency and repetition. It is concluded that three factors underlie ERP variance in the experimental paradigms employed. These factors are word length (physical extent was not dissociated from length in letters), word frequency and semantic similarity. These results may inform issues of experimental control in future studies of ERPs and language processing, may suggest some reassessment of existing studies in which control was not effected for these factors and may have provided a method of wider utility in cognitive neuroscience. The results suggest that systematic information can be derived about the linguistic characteristics of individual words from single word ERPs.

Electroencephalography (EEG) is a valuable tool for clinical and cognitive applications. EEG allows for measuring and imaging of scalp potentials emitted by brain activity and allows researchers to draw conclusions about underlying brain activity and function. However EEG is limited by poor spatial resolution due to various factors. One reason is the fact that EEG electrodes are separated from current sources in the brain by cerebrospinal fluid (CSF), the skull, and the scalp. Unfortunately the conductivities of these tissues are not yet well known which limits the spatial resolution of EEG.Based on prior research, spatial resolution of the EEG can be improved via use of various mathematical techniques that provide increased accuracy of the representation of scalp potentials. One such method is the surface Laplacian. It has been shown to be a direct approach to improving EEG spatial resolution. Yet this approach depends on a geometric head model and much work has been done on assuming the human head to be spherical.In this project, we will develop a mathematical model for ellipsoidal head geometry based on surface Laplacian calculations by Law [1]. The ellipsoidal head model is more realistic to the human head shape and can therefore improve accuracy of the EEG imaging calculations. We will construct a computational program that utilizes the ellipsoidal head geometry in hopes to provide a more accurate representation of data fits compared to the spherical head models. Also, we will demonstrate that the spline surface Laplacian calculations do indeed increase the spatial resolution thereby affording a greater impact to the clinical and cognitive study community involving EEG.
Department of Physics and Astronomy

Thesis (Ed. D.)--Illinois State University, 1987.
Title from title page screen, viewed August 5, 2005. Dissertation Committee: Michael A. Lorber (chair), Larry D. Kennedy, C. Edward Streeter, Wayne Nelsen, Kenneth H. Strand. Includes bibliographical references (leaves 171-194) and abstract. Also available in print.

The possibility to detect reliably human brain signals by small sensors can have substantial impact in healthcare, training, and rehabilitation. This Master the- sis studies Electroencephalography (EEG) wireless sensors, and the properties of their signals. The main goal is to investigate the problem of data interpre- tation accuracy. The measurements provided by small wireless EEG sensors show high variability and high noises, which makes it dicult to interpret the brain signals. The analysis is further exacerbated by the diculty in statistical modeling of these signals. This work presents an attempt to a simple statistical modeling of brain signals. Then, based on such a modeling, an optimal data fusion rule of sensors readings is proposed so to reach a high accuracy in the signal's interpretation. An experimental implementation of the data fusion by real EEG wireless sensors is developed. The experimental results show that the fusion rule provides an error probability of nearly 25% in detecting correctly brain signals. It is concluded that substantial improvements have still to be done to understand the statistical properties of signals and develop optimal decision rules for the detection.

A ubiquitous feature of neural responses is their dependence on stimulus context. One prominent contextual effect is the reduction in neural response size with stimulus repetition, known as “adaptation”. As adaptation is often stimulus-specific, it has been used in visual neuroimaging studies to probe mechanisms of stimulus representation that would otherwise be hidden due to the limited spatial resolution of the available measurement techniques. However, work on the visual system has suggested that stimulus-specific adaptation may not only reflect stimulus representations, but may itself also modify representational information. The four studies described in this report examined the effects of stimulus context on auditory cortical responses using electroencephalography (EEG). The first study used adaptation to examine the neural representation of musical pitch in auditory cortex. Whilst pitch is often treated as a single dimension, namely, the repetition rate of the stimulus waveform, in music, pitch actually has two dimensions: pitch height (the octave in which a note resides) and pitch chroma (the position of the note within an octave). The current study provided evidence for an explicit representation of pitch chroma in an anterolateral region of non-primary auditory cortex. The second, third and fourth studies examined the auditory “mismatch response” (MMR). The MMR refers to the increase in response size to a stimulus when it is presented infrequently (as a “deviant”) compared to when it is presented frequently (as a “standard”). The second study found that the MMR could not be fully accounted for by a passive release from adaptation. Instead, the MMR seemed to reflect a sharpening of the neural representation of the adaptor stimulus with repeated presentation. This suggests that the MMR may be involved in perceptual learning. The third study examined the time courses of the contextual effects on neural responses. Both short- and longer-term effects were observed, with the effects differing between the different components of the auditory evoked response. Notably, the N1 component was influenced by complex effects that seemed to partially reflect the longer-term probabilities of certain short segments of the stimulus sequence, whereas the P2 was influenced by a strong suppressive effect with a remarkably short time course. The fourth study examined whether the contextual effects on auditory-evoked transient and sustained responses are sensitive to the absolute, or the relative, stimulus probabilities. For the transient N1 response, the most striking finding was that adaptation was broadly tuned for deviant stimuli, but sharply tuned for stimuli that were, in terms of their relative probabilities, standards. In contrast, the sustained response appeared to be influenced by a different effect, which facilitated responses to deviant stimuli. The current results suggest that contextual effects differ vastly between different deflections of the auditory-evoked responses, that they include effects that are both complex and long-lasting (of the order of ten seconds or longer), and that they involve not only suppressive, but also facilitatory effects.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2013.
Cataloged from PDF version of thesis. Vita.
Includes bibliographical references.
General anesthesia is a drug-induced, reversible behavioral state characterized by hypnosis (loss of consciousness), amnesia (loss of memory), analgesia (loss of pain perception), akinesia (loss of movement), and hemodynamic stability (stability and control of the cardiovascular, respiratory, and autonomic nervous systems). Each year, more than 25 million patients receive general anesthesia in the United States. Anesthesia-related morbidity is a significant medical problem, including nausea, vomiting, respiratory distress, post-operative cognitive dysfunction, and post-operative recall. To eliminate anesthesia-related morbidity, the brain systems involved in producing general anesthesia must be identified and characterized, and methods must be devised to monitor those brain systems and guide drug administration. A priority for anesthesia research is to identify the brain networks responsible for the characteristic electroencephalography (EEG) signals of anesthesia in relation to sensory, cognitive, memory, and pain systems. In this thesis, we recorded simultaneous intracranial and surface EEG, and single unit data in patients with intractable epilepsy who had been previously implanted with clinical and/or research electrodes. The aims of this research were to characterize the neural signals of anesthesia in a regionally and temporally precise way that is relevant to clinical anesthesia, and to identify dynamic neuronal networks that underlie these signals. We demonstrated region-specific, frequency-band-specific changes in neural recordings at loss of consciousness. We related these findings to theories of how anesthetic drugs may impart their behavioral effects.
by Veronica Sara Weiner.
Ph.D.

The current project presents the hardware implementation and experimental testing of a system that uses electroencephalography (EEG) signals to control the motions of a vehicle through a brain-computer interface device. The user's brain activity is monitored continuously by the NeuroSky MindWave headset, and the EEG signals are processed and provided as inputs to the vehicle control system. The brain functions of interest are the user's attention level, meditation level and ocular blink rate. The values of these signals are transmitted to a microcontroller, which will command the vehicle's motor to initiate motion, stop, or change direction based on the user's brain activity. The current project can find a significant number of applications, since about 17% of the population have disabilities and one million people use wheelchairs, including manually and electrically powered chairs.

Traditionally research into how we perceive our external world focused on the unisensory approach, examining how information is processed by one sense at a time. This produced a vast literature of results revealing how our brains process information from the different senses, from fields such as psychophysics, animal electrophysiology, and neuroimaging. However, we know from our own experiences that we use more than one sense at a time to understand our external world. Therefore to fully understand perception, we must understand not only how the brain processes information from individual sensory modalities, but also how and when this information interacts and combines with information from other modalities. In short, we need to understand the phenomenon of multisensory perception. The work in this thesis describes three experiments aimed to provide new insights into this topic. Specifically, the three experiments presented here focused on examining when and where effects related to multisensory perception emerged in neural signals, and whether or not these effects could be related to behaviour in a time-resolved way and on a trial-by-trial basis. These experiments were carried out using a novel combination of psychophysics, high density electroencephalography (EEG), and advanced computational methods (linear discriminant analysis and mutual information analysis). Experiment 1 (Chapter 3) investigated how behavioural and neural signals are modulated by the reliability of sensory information. Previous work has shown that subjects will weight sensory cues in proportion to their relative reliabilities; high reliability cues are assigned a higher weight and have more influence on the final perceptual estimate, while low reliability cues are assigned a lower weight and have less influence. Despite this widespread finding, it remains unclear when neural correlates of sensory reliability emerge during a trial, and whether or not modulations in neural signals due to reliability relate to modulations in behavioural reweighting. To investigate these questions we used a combination of psychophysics, EEG-based neuroimaging, single-trial decoding, and regression modelling. Subjects performed an audio-visual rate discrimination task where the modality (auditory, visual, audio-visual), stimulus stream rate (8 to 14 Hz), visual reliability (high/low), and congruency in rate between audio-visual stimuli (± 2 Hz) were systematically manipulated. For the behavioural and EEG components (derived using linear discriminant analysis), a set of perceptual and neural weights were calculated for each time point. The behavioural results revealed that participants weighted sensory information based on reliability: as visual reliability decreased, auditory weighting increased. These modulations in perceptual weights emerged early after stimulus onset (48 ms). The EEG data revealed that neural correlates of sensory reliability and perceptual weighting were also evident in decoding signals, and that these occurred surprisingly early in the trial (84 ms). Finally, source localisation suggested that these correlates originated in early sensory (occipital/temporal) and parietal regions respectively. Overall, these results provide the first insights into the temporal dynamics underlying human cue weighting in the brain, and suggest that it is an early, dynamic, and distributed process in the brain. Experiment 2 (Chapter 4) expanded on this work by investigating how oscillatory power was modulated by the reliability of sensory information. To this end, we used a time-frequency approach to analyse the data collected for the work in Chapter 3. Our results showed that significant effects in the theta and alpha bands over fronto-central regions occurred during the same early time windows as a shift in perceptual weighting (100 ms and 250 ms respectively). Specifically, we found that theta power (4 - 6 Hz) was lower and alpha power (10 – 12 Hz) was higher in audio-visual conditions where visual reliability was low, relative to conditions where visual reliability was high. These results suggest that changes in oscillatory power may underlie reliability based cue weighting in the brain, and that these changes occur early during the sensory integration process. Finally, Experiment 3 (Chapter 5) moved away from examining reliability based cue weighting and focused on investigating cases where spatially and temporally incongruent auditory and visual cues interact to affect behaviour. Known collectively as “cross-modal associations”, past work has shown that observers have preferred and non-preferred stimuli pairings. For example, subjects will frequently pair high pitched tones with small objects and low pitched tones with large objects. However it is still unclear when and where these associations are reflected in neural signals, and whether they emerge at an early perceptual level or later decisional level. To investigate these questions we used a modified version of the implicit association test (IAT) to examine the modulation of behavioural and neural signals underlying an auditory pitch – visual size cross modal association. Congruency was manipulated by assigning two stimuli (one auditory and one visual) to each of the left or right response keys and changing this assignment across blocks to create congruent (left key: high tone – small circle, right key: low tone – large circle) and incongruent (left key: low tone – small circle, right key: high tone – large circle) pairings of stimuli. On each trial, subjects were presented with only one of the four stimuli (auditory high tone, auditory low tone, visual small circle, visual large circle), and asked to respond which was presented as quickly and accurately as possible. The key assumption with such a design is that subjects should respond faster when associated (i.e. congruent) stimuli are assigned to the same response key than when two non-associated stimuli are. In line with this, our behavioural results demonstrated that subjects responded faster on blocks where congruent pairings of stimuli were assigned to the response keys (high pitch-small circle and low pitch large circle), than blocks where incongruent pairings were. The EEG results demonstrated that information about auditory pitch and visual size could be extracted from neural signals using two approaches to single-trial analysis (linear discriminant analysis and mutual information analysis) early during the trial (50ms), with the strongest information contained over posterior and temporal electrodes for auditory trials, and posterior electrodes for visual trials. EEG components related to auditory pitch were significantly modulated by cross-modal congruency over temporal and frontal regions early in the trial (~100ms), while EEG components related to visual size were modulated later (~220ms) over frontal and temporal electrodes. For the auditory trials, these EEG components were significantly predictive of single trial reaction times, yet for the visual trials the components were not. As a result, the data support an early and short-latency origin of cross-modal associations, and suggest that these may originate in a bottom-up manner during early sensory processing rather than from high-level inference processes. Importantly, the findings were consistent across both analysis methods, suggesting these effects are robust. To summarise, the results across all three experiments showed that it is possible to extract meaningful, single-trial information from the EEG signal and relate it to behaviour on a time resolved basis. As a result, the work presented here steps beyond previous studies to provide new insights into the temporal dynamics of audio-visual perception in the brain.
All experiments, although employing different paradigms and investigating different processes, showed early neural correlates related to audio-visual perception emerging in neural signals across early sensory, parietal, and frontal regions. Together, these results provide support for the prevailing modern view that the entire cortex is essentially multisensory and that multisensory effects can emerge at all stages during the perceptual process.

Model order reduction is a technique to reduce computational times of parameterized PDEs while maintaining good accuracy of the approximated solution. Reduced basis methods (RB) are the most common algorithms for reducing the complexity of parameterized PDEs and nowadays they are widely applied and very actively researched in numerous fields. We propose two ideas to further enhance model reduction: the Fundamental Order Reduction Method (FOR) and offline error estimators for RB methods. The FOR method uses nonlinear combinations of the solutions to build the reduced model and use simple affine evaluations to execute the online stage. On the other hand, offline estimators are a class of estimators that move a-posteriori operations to the offline stage, reducing in this way the load of computations in the online stage. We apply these two ideas to an EEG equation which is useful for detecting the position where an epilepsy seizure begins inside the brain. We present two known ways to solve this equation: direct approach and subtraction approach, and show theoretical and numerical results of the application of the RB and FOR methods. We prove that is not feasible to apply model reduction in the direct approach but show that it is possible in the subtraction approach. Afterwards we solve the inverse problem associated with the EEG equation using a combination of the FOR method and neural networks.

Model order reduction is a technique to reduce computational times of parameterized PDEs while maintaining good accuracy of the approximated solution. Reduced basis methods (RB) are the most common algorithms for reducing the complexity of parameterized PDEs and nowadays they are widely applied and very actively researched in numerous fields. We propose two ideas to further enhance model reduction: the Fundamental Order Reduction Method (FOR) and offline error estimators for RB methods. The FOR method uses nonlinear combinations of the solutions to build the reduced model and use simple affine evaluations to execute the online stage. On the other hand, offline estimators are a class of estimators that move a-posteriori operations to the offline stage, reducing in this way the load of computations in the online stage. We apply these two ideas to an EEG equation which is useful for detecting the position where an epilepsy seizure begins inside the brain. We present two known ways to solve this equation: direct approach and subtraction approach, and show theoretical and numerical results of the application of the RB and FOR methods. We prove that is not feasible to apply model reduction in the direct approach but show that it is possible in the subtraction approach. Afterwards we solve the inverse problem associated with the EEG equation using a combination of the FOR method and neural networks.

Introduction Combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. The combination of these technologies provides informations and details on the spatio-temporal aspects of human brain processing. fMRI has an excellent spatial resolution and allows the localization of brain regions in which there is a change in the level of neuronal activity during an experimental condition compared to a control condition. In contrast, EEG measures neuronal currents directly from the subject’s scalp with a high temporal resolution in the range of milliseconds. Combined recording wants to overcome the spatial limitations of EEG and the temporal limitations of fMRI, using their complementary features. For instance, combined EEG-fMRI technique can be used to identify the neural correlates of clinically or behaviourally important spontaneous EEG activity, such as interictal spikes, the alpha rhythm and sleep waves. The presurgical evaluation of patients with epilepsy is one of the areas where combining EEG and fMRI has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. fMRI is mostly used in the study of sensory, motor and cognitive functions, where there is a difference between experimental condition and control condition. In the context of epilepsy, one can consider the control condition to occur when the EEG is at baseline and experimental condition to correspond to the presence of an epileptic discharge. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG which are used in conjunction with a General Linear Model (GLM) approach to analyze fMRI data. A model is obtained by the convolution of the EEG events, which are represented as stick functions of unitary amplitude, with a model of the event-related fMRI response, represents by the haemodynamic response function (HRF); maps showing regions of significant IED-related change are obtained through voxel-wise fitting of the model and application of appropriate statistical thresholds. In this thesis we present an easy to use approach for combined EEG-fMRI analysis developed to improve the identification of the IEDs. The novel automatic method is based on Independent Component Analysis (ICA) and allows to detect IED activity in order to use it as a parametric modulator in fMRI analysis. The Novel Method Data quality is a crucial issue in multimodal functional imaging and data integration. Both fMRI and EEG data acquisition processes can severely affect the other’s performance through electromagnetic interactions, therefore the pre-processing is necessary for both EEG and fMRI data. While for fMRI data the pre-processing is generally standard, apart from the choice of spatial smoothing; the EEG pre-processing requires a complex and not one-way procedure to remove the artifacts. In literature different methods have been developed to remove gradient and pulse artifacts, considering both hardware and software solutions. The gradient EEG artifact removal method implemented in our EEG system acquisition did not give completely satisfactory results; so we decided to developed a novel method. Since the project regarding the gradient filter started together with the novel EEG-fMRI integration method and the analysis on patients with partial epilepsy are still in progress to avoid the introduction of a further variable in the validation of the method we decided to use the algorithms implemented in the SystemPlus software. After a pre-processing applied on EEG data and composed by a re-reference and filtering, a method based on ICA decomposition was applied. In the field of biomedical signal processing, Blind Source Separation (BSS) methods are generally used to separate multi-channel recordings into their constituent components; ICA is a subset of such techniques used to separate statistically independent components from a mixture of data. ICA decomposition of the data was performed using FastICA algorithm implemented in EEGLAB. The novel method consists in four fundamental steps: • Selection of components • Reconstruction of EEG signal • Selection of channel and FFT analysis • Construction of EEG regressor The crucial point is the selection of components. To select the components related to IED activity, we used a time-frequency representation obtained by using wavelet-based analysis. We computed the wavelet power for all the components in the epochs of interest and then, for each component, we selected from the frequency bins the one with the maximal power over total recording session. Finally power was averaged along time, obtaining one value for each component. Components that exceeded mean value ± standard deviation were chosen for further analysis. After the components of interest have been selected, they were back projected to obtain a new EEG signal (reconstructed EEG). A Fast Fourier Transform (FFT) analysis was applied on the time series of the selected channel (where the IED activity is clearly visible) for epochs acquired during each fMRI volume. Then the power time course created for all volumes was used to form the EEG regressor used in GLM analysis. Discussion The aim of the research project here described is the development of an innovative procedure for integrating neurophysiological and functional neuroimaging data. In fMRI processing the selection of the experimental paradigm as difference between task and rest conditions is of great importance, in fact the information related to the experimental events and to the rest condition are to be used as input in GLM analysis. Regressors of interest are typically obtained by convolving impulses or boxcar functions, which are representations of the events or conditions of interest, with a model of the BOLD response (HRF). In the study of spontaneous EEG activity without a task condition we can use the EEG signal to derive the input for GLM. In literature several methods for the analysis of simultaneous acquired EEG-fMRI data are proposed. The aim is to find regions of BOLD change linked to the discharges. In the conventional approach each event is marked by visual inspection of the EEG data recorded in the scanner, then a series of identical impulses functions (delta functions) are created and convolved with a canonical HRF, obtaining the regressor for a GLM. The methods presented in Formaggio et al., 2008 and Manganotti et al., 2008 are two attempts of EEG and fMRI integration. However in the first study signals were recorded simultaneous but their correlation analysis was as whether they were recorded in separate sessions, while in the second one we used a conventional approach based on the creation of the regressor as a set of stick functions representing the timing of IED activity. Hence the necessity to developed a new method of integration. The new method aimed to improve upon existing methods since the epileptiform activity, recorded from a scalp EEG, is used to modulate changes in BOLD signal. ICA decomposition is used to identify signals representing activity of interest but one of the major difficulties is their identification. We proposed an automatic selection based on wavelet analysis, because typically IEDs activity is higher in amplitude than background activity and its power increases. The reconstructed EEG signal is obtained with the only contribution of the selected components, method used in many studies to remove artifact from EEG traces. Like in the resting state studies, where alpha rhythm or its spectrum is used as a regressor in GLM analysis, the power time series of EEG signal is used as GLM input. Using conventional approach each event is treated as equal, although epileptic spikes may vary in amplitude, duration and also in appearance. They ignore the fact that IED activity is continuous and contains also fluctuating subthreshold epileptic activity, not clearly seen on surface EEG recordings. In contrast, such meaningful information is contained in the ICA factors employed in our method. Analysis of in silico data validates the method, since demonstrates the reliability of reconstructed IED regressor. All five patients with partial epilepsy we enrolled in this study had frequent interictal focal slow wave activity on routine EEG. In all continuous EEG-fMRI recording sessions, after fMRI artifact removal, we obtained a good quality EEG that allowed us to detect spontaneous IEDs and analyze the related BOLD activation. In their focal distribution, these BOLD activations resembled the focal IEDs seen on routine scalp EEG and EEG recorded during EEG-fMRI sessions; and they are in agreement with the clinical history of the patients. We plan to increase the number of patients and also test this method on EEG with various patterns other than the epileptiform discharges, for example in resting state analysis where, like in the context of epilepsy, the activation task used to drive GLM analysis is missing. For this reason EEG signal is necessary to evaluate hemodynamic changes in fMRI and its analysis is fundamental to derive informations on the electrical activity. Even if it is believed that the HRF to epileptic spikes does not vary significantly from that to external stimuli, HRF could shows different peak times or even non canonical shape in the epileptogenic zone. This observation may be advanced as a working hypothesis for further investigating the choice of HRF in patients with epilepsy; future developments possibly involve a study of BOLD signal in this category of patients, and its relation with the electrical activity. In this way the sensitivity of EEG-fMRI studies in epilepsy could be improved with the use of different HRFs. Moreover, in the future, we will test the integration method to data filtered with the new algorithm in order to conclude this project.
Introduzione La registrazione simultanea fra l’elettroencefalogramma (EEG) e la risonanza magnetica funzionale (fMRI) è un importante strumento nel campo del neuroimaging funzionale che unisce l’alta risoluzione spaziale delle immagini fMRI (1-2 mm) con l’alta risoluzione temporale dell’EEG (ms). Registrare il segnale EEG durante l’acquisizione di immagini fMRI permette di identificare l’attività cerebrale e di ottenere informazioni localizzatorie sui generatori di tale attività. Nonostante i numerosi problemi legati alla presenza di artefatti sul segnale e sulle immagini, dovuti all’interazione fra le due apparecchiature, tale metodica si sta affermando e rafforzando all’interno delle neuroscienze. I campi di applicazioni sono diversi e in particolare la coregistrazione EEG-fMRI può essere utilizzata per studiare e descrivere l’attività elettrica spontanea durante una condizione di riposo (resting state), durante il sonno o causata da forme di epilessia. Molti pazienti con una forma di epilessia farmaco-resistente non possono sottoporsi ad un intervento chirurgico, in quanto la semplice risonanza magnetica non permette l’individuazione della sorgente epilettogena. In questo senso la registrazione simultanea dell’EEG e della fMRI permetterebbe l’identificazione di una possibile sorgente, legata direttamente all’attività elettrica del paziente. Il cambiamento dell’attività neuronale, infatti, è associato ad un cambiamento del rapporto di concentrazione nel sangue fra l’emoglobina ossigenata e quella deossigenata e tale cambiamento può essere misurato attraverso l’effetto BOLD (Blood Oxygen Level Dependent). Le attivazioni cerebrali, infatti, sono date da alterazioni coordinate dell’attività elettrica regionale e del flusso sanguigno cerebrale. La tecnica di coregistrazione EEG-fMRI permette di evidenziare, nel momento in cui si verifica un evento elettrico, un’area di alterato contenuto di desossiemoglobina dovuta ad un aumentato afflusso ematico nella zona cerebrale che genera tale segnale EEG. In genere l’fMRI è usata in studi in cui è presente una condizione sperimentale che differisce da una condizione di riposo, entrambe controllate da un operatore. Il principio base dell’analisi fMRI è il confronto tra un’attività basale cerebrale ed un’attività dovuta ad un evento da studiare (spontaneo o evocato), al fine di ottenere una variazione relativa di flusso ematico. Nello studio dell’epilessia si può considerare l’EEG a riposo come condizione di controllo mentre come condizione sperimentale può essere usato il segnale EEG caratterizzato dalla presenza di eventi parossistici (crisi o attività intercritica). L’analisi convenzionale applicata ai dati EEG-fMRI consiste nell’individuazione visiva da parte del neurologo degli intervalli temporali di interesse, che caratterizzano l’attività intercritica del paziente. Dalla convoluzione degli eventi, rappresentati matematicamente da impulsi, con un modello di risposta emodinamica (haemodynamic response function: HRF), si ottiene il regressore utilizzato nell’analisi General Linear Model (GLM). Si producono così mappe di elevata risoluzione spaziale delle aree cerebrali che generano l’evento patologico osservato. Inoltre l’EEG-fMRI associata ad altre metodiche come video-EEG, risonanza magnetica nucleare (RMN) convenzionale, tomografia computerizzata ad emissione di fotoni singoli (SPECT), tomografia ad emissione di positroni (PET), spettroscopia ecc. contribuisce allo studio di pazienti epilettici candidati alla terapia chirurgica. Lo scopo della presente tesi è quello di sviluppare un metodo automatico, basato sull’analisi delle componenti indipendenti (ICA), per individuare l’attività intercritica in esame, al fine di utilizzare il segnale EEG in toto per la generazione di mappe di attivazione fMRI. Il Nuovo Metodo La qualità dei dati è molto importante nel processo di integrazione; pertanto è necessario applicare un pre-processing ad entrambe le tipologie di dati. Mentre tale elaborazione è standard per i dati fMRI, non lo è per i dati EEG. In letteratura sono stati sviluppati diversi metodi per rimuovere l’artefatto da gradiente di campo magnetico e quello da pulsazione cardiaca. Il metodo per la rimozione dell’artefatto da gradiente implementato nel nostro sistema di acquisizione EEG non ha dato dei risultati completamente soddisfacenti in alcune situazioni. Pertanto è stato necessario implementare un nuovo metodo. Tuttavia l’implementazione di questo nuovo filtro è iniziata contemporaneamente all’implementazione del nuovo metodo di integrazione EEG-fMRI e la sua applicazione su segnali di pazienti epilettici è ancora in atto. Per questi motivi e per non introdurre ulteriori variabili nella validazione del metodo di integrazione, è stato deciso di utilizzare l’algoritmo implementato nel software di acquisizione EEG. In seguito ad un pre-processamento dei dati, caratterizzato da un cambio di referenza e da opportuni filtraggi, è stato applicato il metodo delle componenti indipendenti. L’ICA è una tecnica statistica che permette di individuare le componenti che stanno alla base di una serie multidimensionale di dati, assumendo che le sorgenti siano statisticamente indipendenti e la loro distribuzione non sia gaussiana. Tale analisi è stata effettuata utilizzando l’algoritmo FastICA implementato in EEGLAB ed ha prodotto un numero di componenti per ciascun tracciato pari al numero dei canali EEG. Il nuovo metodo può essere suddiviso in 4 passaggi: • Selezione delle componenti • Ricostruzione del segnale EEG • Selezione del canale ed analisi FFT • Costruzione del regressore EEG Il punto cruciale è la scelta delle componenti che descrivono l’attività intercritica in esame. Per ogni componente si è calcolata la trasformata wavelet continua negli intervalli di interesse che fornisce i valori di potenza nel tempo in funzione della frequenza. Selezionando la frequenza massima si è ottenuto un segnale dipendente esclusivamente dal tempo. Successivamente è stato calcolato il valore medio nell’intervallo temporale e sono state scelte le componenti con più elevata potenza. In seguito si è ricostruito il segnale EEG utilizzando solo il contributo delle componenti scelte. E’ stata applicata un’analisi in frequenza utilizzando la Fast Fourier Transform (FFT) ad epoche di durata pari al tempo di acquisizione di un volume di fMRI; la potenza ottenuta è stata convoluta con la risposta emodinamica scelta ottenendo un modello chiamato ‘regressore’ usato successivamente nella stima GLM dell’analisi fMRI. Questo metodo è stato validato utilizzando dati simulati, ed in seguito applicato a due datasets: il primo composto da due soggetti sani a cui è stata fatta la coregistrazione EEG-fMRI durante apertura e chiusura degli occhi, il secondo composto da 5 pazienti con epilessia parziale a cui è stata fatta la registrazione simultanea in condizione di riposo. L’applicazione del metodo ai dati simulati ha portato alla sua validazione. In tutte e tre le simulazioni si sono ottenute delle forme d’onda, rappresentanti i regressori, molto simili ai regressori assunti come “veri”. Nei due soggetti sani, che hanno svolto un task di apertura e chiusura degli occhi, l’analisi ha prodotto un’attivazione degli occhi ed una deattivazione occipitale, in accordo con i networks ormai noti dalla letteratura. Per quanto riguarda i pazienti, l’integrazione dei due segnali ha portato ad attivazioni concordi con l’attività elettrica e con il loro quadro clinico in 4 pazienti su 5. Le componenti scelte in base al metodo rispecchiano visivamente l’attività parossistica visibile nel tracciato EEG registrato durante acquisizione fMRI e confrontato con l’EEG standard acquisito di routine. Discussione In questo lavoro è stato presentato un nuovo metodo di integrazione fra un segnale neurofisiologico (EEG) e dati di neuroimaging funzionale (fMRI), basato sull’analisi delle componenti indipendenti. Il paradigma sperimentale (protocollo) è un dato molto importante per l’analisi fMRI, infatti le informazioni legate al task e alla condizione di riposo sono utilizzate come ingresso nell’analisi GLM. In assenza di un task, come nello studio dell’epilessia, è necessario utilizzare il segnale EEG per pilotare l’analisi GLM. In letteratura sono stati proposti diversi metodi di integrazione. Nell’approccio convenzionale il protocollo, formato dagli intervalli temporali degli eventi di interesse individuati in seguito ad ispezione visiva, viene convoluto con un modello di risposta emodinamica, ottenendo il regressore per l’analisi GLM. I metodi presentati in Formaggio et al., 2008 e in Manganotti et al., 2008 rappresentano due primi tentativi di integrazione. Tuttavia nel primo studio i segnali vengono analizzati come se fossero stati acquisiti in due sessioni separate, mentre nel secondo studio viene utilizzato l’approccio convenzionale. Da qui la necessità di sviluppare un nuovo metodo di integrazione. Il nuovo metodo ha lo scopo di migliorare quelli già esistenti sfruttando l’informazione derivante da tutto il segnale EEG e non tenendo conto dei soli intervalli temporali di interesse. Il punto cruciale è l’identificazione del segnale legato all’attività di interesse. E’ stato proposto un metodo automatico per facilitare tale scelta, basato sulle trasformate wavelet e valorizzando il contenuto energetico del segnale. Il segnale EEG ricostruito è ottenuto con il solo contributo delle componenti scelte ed in fine la sua potenza spettrale viene utilizzata come ingresso nell’analisi GLM. Uno degli scopi futuri sarà quello di aumentare il numero dei pazienti e di testare il metodo anche su altre tipologie di EEG, come ad esempio quello legato alla condizione di resting state. Anche in questo caso, infatti, manca la presenza di un task che possa pilotare l’analisi GLM, e l’EEG risulta l’unico strumento di informazione per poter arrivare a delle mappe di attivazione. Un ulteriore progetto futuro è legato alla scelta della risposta emodinamica HRF. Tale risposta potrebbe non essere identica a quella ottenuta in seguito ad un task o ad uno stimolo esterno; il suo picco e la sua forma potrebbero infatti essere diversi nella zona epilettogena. In questo senso la sensibilità degli studi EEG-fMRI nell’epilessia potrebbe migliorare utilizzando diverse HRF. In fine verrà applicato il nuovo metodo di integrazione a dati EEG filtrati con il nuovo algoritmo sviluppato.

Quantitative electroencephalography (QEEG) was used in this study to describe cognitive processing, particularly brain locations used, during performance of the Wisconsin Card Sorting Test (WCST). The hypothesis was that significant cognitive functioning is not limited to the frontal lobes. Significant EEG activity was found in non-frontal areas as well as frontal areas.

The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing method which was based on a simple neurological model that assumed an additive outlier nature of event related potentials to the ongoing EEG process. When event related potentials, such as motor related potentials, are generated by a unique additional process they are "added" into the ongoing process and hence, will appear as additive outlier content when considered from the point of view of the ongoing process. By modeling the EEG with AR models with robustly estimated (GM-estimates) parameters and by using those models in a robust signal estimator, a "cleaned" EEG signal is obtained. The outlier content, data that is extracted from the EEG during cleaning, is then processed to yield event related information. The EEG from four subjects formed the basis of the initial investigation into the viability of this single trial processing scheme. The EEG was collected under two conditions: an active task in which subjects performed a skilled thumb movement and an idle task in which subjects remained alert but did not carry out any motor activity. The outlier content was processed which provided single trial outlier waveforms. In the active case these waveforms possessed consistent features which were found to be related to events in the individual thumb movements. In the idle case the waveforms did not contain consistent features. Bayesian classification of active trials versus idle trials was carried out using a cost statistic resulting from the application of dynamic time warping to the outlier waveforms. Across the four subjects, when the decision boundary was set with the cost of misclassification equal, 93% of the active trials were classified correctly and 18% of the idle trials were incorrectly classified as active. When the cost of misclassifying an idle trial was set to be five times greater, 80% of the active trials were classified correctly and only 1.7% of the idle trials were incorrectly classified as active.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

Thesis (Ph. D.)--University of Oregon, 2007.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 126-133). Also available for download via the World Wide Web; free to University of Oregon users.

Alpha neurofeedback training has been put forward for use in the optimal performance field as a way to enhance cognitive abilities and musical performance amongst others. The literature to date, however, has been characterised by methodological limitations and disagreement on procedural and analytic matters which makes drawing conclusions and comparing results problematic. To provide clarity to the field, and to enable effective investigation of the usefulness of alpha neurofeedback training in the realm of optimal performance, it would be useful if a standardised way of conducting alpha neurofeedback was established. It is unclear, for instance, what influence the current variations have on participants’ ability to train their alpha and to the outcome (e.g. on cognition) of their performance. This thesis therefore sets out to investigate whether there is an optimum methodology for alpha neurofeedback training. The first experiment was designed to establish an index of learning to use in the successive experiments; that is, to establish how alpha should be measured and how participants’ performance should be analysed. Fifty-two participants were given 10 sessions of once weekly alpha (8-12Hz) enhancement and alpha suppression training at Pz. From the results of this first experiment it was decided that amplitude and per cent time would be the measures used to investigate participants’ performance and that analyses of participants’ performance both within and across sessions would be examined. Further, it was decided that baseline measures needed to be incorporated in to the analyses in order to establish a clearer picture of participants’ ability to learn. Experiment 2 involved training 33 participants to both enhance and suppress their alpha (8-12Hz) at Pz. Over the course of 10 once weekly sessions, 17 participants trained with their eyes open and 16 were trained with their eyes closed. The results suggested that eyes open alpha neurofeedback training is a more optimal training paradigm than eyes closed. The third experiment therefore set out to examine whether the type of eyes open training has an influence on participants’ performance. Specifically, 15 participants were given audio feedback, 15 were given audio-visual feedback, and 17 were given visual feedback over the course of 10 once weekly alpha (8-12Hz) enhancement and alpha suppression sessions. The results showed that of the 3 types of feedback, audio feedback produced the more optimal results. Although there are further aspects of methodology and analysis to be investigated, the results from this thesis suggest that these fundamental design decisions do make a difference to the participants’ ability to exert a conscious control over their own EEG alpha activity suggesting that there is, in fact, an optimum methodology for alpha (8-12Hz) neurofeedback training.

The fractal dimension of the phase space representation of the EEG of 8 clinical patients undergoing general anesthesia is determined. An attempt is made to correlate the trends in EEG dimensional complexity with the depth of anesthesia. While the EEG showed marked changes in dimensional complexity when passing through anesthetic stages, a uniform depth-dimension trend consistent with the eight patients was not observed. The only significant trend observed was a distinct change in dimension for individual EEG's with anesthesia. The dimensional complexities measured were very high and non-convergent, so that the presence of a fractal attractor in the EEG was not evident in this analysis. The observed trends were the close correlation of both brain hemispheres simultaneously exhibiting the same degree of dimensional complexity, and the close correlation of change in EEG spectral edge frequency with change in dimension with anesthesia.

Monitoring bio-electric events is a common procedure, which provides medical data required in clinical and research applications. Electrophysiological measurements are applied in diagnosis as well as evaluation of the performance of different body organs and systems, e. g. the heart, muscles and the nervous system. Furthermore, it is staple feature in operation rooms and extensive care units. The performance of the recording system is affected by the tools and instrumentation used and the bio-electrode is a key-player in electrophysiology, hence, the improvements in the electrode recording technique will be directly reflected in the system?s performance in terms of the signal quality, recording duration as well as patient comfort. In this thesis, a design methodology for micro-spike array dry bio-electrodes is introduced.

The purpose of this methodology is to meet the design specifications for portable long-term EEG recording and optimize the electrical performance of the electrodes by maximizing the electrode-skin contact surface area, while fulfilling design constraints including mechanical, physiological and economical limitations. This was followed by proposing a low cost fabrication technique to implement the electrodes. The proposed electrode design has a potential impact in enhancing the performance of the current recording systems, and also suits portable monitoring and long term recording devices. The design process was aided by using a software design and optimization tool, which was specifically created for this application.

The application conditions added challenges to the electrode design in order to meet the required performance requirements. On the other hand, the required design specifications are not fulfilled in the current electrode technologies which are designed and customized only for short term clinical recordings.

The electrode theory of application was verified using an experimental setup for an electrochemical cell, but the overall performance including measuring the electrode impedance is awaiting a clinical trial.

Alzheimer’s is one of the most costly illnesses that exists today and the number of people with alzheimers diease is expected to increase with 100 million until the year 2050. The medication that exists today is most effective if Alzheimer’s is detected during early stages since these medications do not cure Alzheimer’s but slows down the progression of the disease. Electroencephalography (EEG) is a relatively cheap method in comparison to for example Magnetic Resonance Imaging when it comes to diagnostic tools. However it is not clear how to deduce whether a patient has Alzheimer’s disease just from EEG data when the analyst is a human. This is the underlying motivation for our investigation; can supervised machine learning methods be used for pattern recognition using only the spectral power of EEG data to tell whether an individual has alzheimer’s disease or not? The output accuracy of the trained supervised machine learning models showed an average accuracy of above 80%. This indicates that there is a difference in the neural oscillations of the brain between healthy individuals and alzheimer’s disease patients which the machine learning methods are able to detect using pattern recognition.
Alzheimer är en av de mest kostsamma sjukdomar som existerar idag och antalet människor med alzheimer förväntas öka med omkring 100 miljoner människor tills 2050. Den medicinska hjälp som finns tillgänglig idag är som mest effektiv om man upptäcker Alzheimer i ett tidigt stadium eftersom dagens mediciner inte botar sjukdomen utan fungerar som bromsmedicin. Elektroencefalografi är en relativt billig metod för diagnostisering jämfört med Magnetisk resonanstomografi. Det är emellertid inte tydligt hur en läkare eller annan tränad individ ska tolka EEG datan för att kunna avgöra om det är en patient med alzheimers som de kollar på. Så den bakomliggande motivation till vår undersökning är; Kan man med hjälp av övervakad maskininlärning i kombination med spektral kraft från EEG datorn skapa modeller som kan avgöra om en patient har alzheimers eller inte. Medelvärdet av våra modellers noggrannhet var över 80%. Detta tyder på att det finns en faktiskt skillnad mellan hjärna signalerna hos en patient med alzheimer och en frisk individ, och att man med hjälp av maskininlärning kan hitta dessa skillnader som en människa enkelt missar.

The objective of this investigation was to evaluate quantitative electroencephalography (EEG) as a measure of CNS stimulation. The reproducibility and sensitivity of quantitative EEG was compared to neuroendocrine, mood, and psychomotor performance measures. The study was conducted in two parts. The first part investigated the inter- and intra-individual variability associated with a series of pharmacological response measures under baseline (no drug) conditions. It was an open-label pilot study in which eight healthy male volunteers underwent a series of tests (EEG, visual continuous performance task (CPT), a finger tapping task, and self-rated mood scales) repeated eight times over a 12 hour period on three occasions, one week apart. The second part evaluated the sensitivity of quantitative EEG to dextroamphetamine (DA) compared to other response measures. It was a double-blind, placebo-controlled, four-period crossover study in eight healthy male volunteers. Subjects received 5 mg, 10 mg, or 20 mg DA or placebo orally, and underwent the same series of tests as well as blood collection for serum prolactin and DA determination, eight times over a 12 hour period. A GC method allowing quantitation of 2ng/mL DA in serum was developed. The greatest between-day, within-day, and intrasubject variability was associated with quantitative EEG. Learning effects were observed for the psychometric tests, and first session effects were apparent for several of the tests including the EEG. EEG response to DA was observed only in the 3 subjects who had baseline alpha activity greater than 35%. There was a statistically significant decrease in serum prolactin levels after DA administration, with the largest decrease observed after the 5 mg dose. Mood scales showed that 3 of 9 subjects experienced dysphoria after DA dosing. The effect on mood was generally greater as the dose increased. One subject was discontinued from the study because he experienced intense dysphoria after the 5 mg dose. Doses could not be distinguished based on the results of the psychometric tests. Effects on mood, serum prolactin levels, and performance as measured by CPI and finger tapping were not correlated with the EEG changes observed. Pharmacokinetic evaluation showed that the rate of DA absorption appears to decrease as the dose increases. Quantitative EEG conducted under our study conditions and study population was not more sensitive for the assessment of CNS stimulation than the other response measures evaluated. The sensitivity may be improved by screening volunteers to select subjects with higher background alpha activity.

Copies of author's previously published articles inserted. Bibliography: leaves I-LXIV. Assesses the diagnostic and prognostic value of early electroencephalography (EEG) and somatosensory evoked potential studies in cortical and non-cortical ischaemic stroke. Both conventional and topographic/quantitative studies were performed. A parallel study was carried out on healthy volunteers to provide an effective control. Equipment and quantitative EEG (qEEG) variability was also assessed.

Interictal spikes are spontaneous neuronal discharges that occur between epileptic seizures, and that constitute a specific marker of epilepsy. The topic of our doctoral research is to localize in a noninvasive manner the regions of the brain responsible for generating the spikes. Such information is of great interest in the presurgical evaluation for pharmacoresistant epilepsy.
In this context, we have studied the possibility of using a combination of two techniques, namely electroencephalography (EEG) and magnetic resonance imaging (MRI). Specifically, we have investigated three tracks for the combination of EEG and MRI.
First, anatomical information from MRI can be used for improving EEG source localization. Our example is the modelling of postsurgical brain and skull defects, which affect the conductive properties of the head.
Second, the EEG can be recorded inside the MR scanner and thereby allows the investigation of spontaneous epileptic spikes with functional MRI (fMRI). We evaluated the quality of EEG within the scanner, and measured the spatial and temporal fMRI response to spikes.
Third, the information from the two modalities can be combined in order to benefit from both the good spatial resolution of fMRI and the excellent temporal resolution of EEG. We have proposed to build statistical maps for EEG source localization in order to identify common areas of activation in EEG and fMRI.