Dissertations / Theses on the topic 'Electroencephalography (EEG)'

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.

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

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

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.

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.

A new method for the estimation of the EEG evoked potential (EP) is presented in this thesis. This method is based on a new model of the EEG response which is assumed to be the sum of the EP and independent correlated Gaussian noise representing the spontaneous EEG activity. The EP is assumed to vary in both shape and latency, with the shape variation represented by correlated Gaussian noise which is modulated by the EP. The latency of the EP is also assumed to vary over the ensemble of responses in a random manner governed by some unspecified probability density. No assumption on stationarity is needed for the noise.
With the model described in state-space form, a Kalman filter is constructed, and the variance of the innovation process of the response measurements is derived. A maximum likelihood solution to the EP estimation problem is then obtained via this innovation process.
Tests using simulated responses show that the method is effective in estimating the EP signal at signal-to-noise ratio as low as -6db. Other tests using real normal visual response data yield reasonably consistent EP estimates whose main components are narrower and larger than the ensemble average. In addition, the likelihood function obtained by our method can be used as a discriminant between normal and abnormal responses, and it requires smaller ensembles than other methods.

Thesis advisor: Robert Signorile
It has long been known that as neurons fire within the brain they produce measurable electrical activity. Electroencephalography (EEG) is the measurement and recording of these electrical signals using sensors arrayed across the scalp. Though there is copious research in using EEG technology in the fields of neuroscience and cognitive psychology, it is only recently that the possibility of utilizing EEG measurements as inputs in the control of computers has emerged. The idea of Brain-Computer Interfaces (BCIs) which allow the control of devices using brain signals evolved from the realm of science fiction to simple devices that currently exist. BCIs naturally present themselves to many extremely useful applications including prosthetic devices, restoring or aiding in communication and hearing, military applications, video gaming and virtual reality, and robotic control, and have the possibility of significantly improving the quality of life of many disabled individuals. However, current BCIs suffer from many problems including inaccuracies, delays between thought, detection, and action, exorbitant costs, and invasive surgeries. The purpose of this research is to examine the Emotiv EPOC© System as a cost-effective gateway to non-invasive portable EEG measurements and utilize it to build a thought-based BCI to control the Parallax Scribbler® robot. This research furthers the analysis of the current pros and cons of EEG technology as it pertains to BCIs and offers a glimpse of the future potential capabilities of BCI systems
Thesis (BA) — Boston College, 2010
Submitted to: Boston College. College of Arts and Sciences
Discipline: Computer Science Honors Program
Discipline: Computer Science

This thesis addresses some theoretical issues generated by the results of recent analysis of EEG time series proving the brain dynamics are driven by abrupt changes making them depart from the ordinary Poisson condition. These changes are renewal, unpredictable and non-ergodic. We refer to them as crucial events. How is it possible that this form of randomness be compatible with the generation of waves, for instance alpha waves, whose observation seems to suggest the opposite view the brain is characterized by surprisingly extended coherence? To shed light into this apparently irretrievable contradiction we propose a model based on a generalized form of Langevin equation under the influence of a periodic stimulus. We assume that there exist two different forms of time, a subjective form compatible with Poisson statistical physical and an objective form that is accessible to experimental observation. The transition from the former to the latter form is determined by the brain dynamics interpreted as emerging from the cooperative interaction among many units that, in the absence of cooperation would generate Poisson fluctuations. We call natural time the brain internal time and we make the assumption that in the natural time representation the time evolution of the EEG variable y(t) is determined by a Langevin equation perturbed by a periodic process that in this time representation is hardly distinguishable from an erratic process. We show that the representation of this random process in the experimental time scale is characterized by a surprisingly extended coherence. We show that this model generates a sequence of damped oscillations with a time behavior that is remarkably similar to that derived from the analysis of real EEG's. The main result of this research work is that the existence of crucial events is not incompatible with the alpha wave coherence. In addition to this important result, we find another result that may help our group, or any other research group working on the analysis of brain's dynamics, to prove or to disprove the existence of crucial events. We study the diffusion process generated by fluctuations emerging from the same model after filtering out the alpha coherence, and we study the recursion to the origin. We study the survival probability of this process, namely the probability that up to a given time no re-crossing of the origin occurs. We find that this is an inverse power law with a power that depends on whether or not crucial events exist.

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.

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Bachelors
Arts and Sciences
Psychology

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.

Modeling of brain activity is often seen as requiring great computing power. However in the special case of modeling scalp EEG it is possible to adopt a continuum approximation for the cortex, and then to use the techniques of wave physics to describe its consequent large-scale dynamics. The model incorporates the following critical components: two classes of neurons (excitatory and inhibitory), the typical number and strength of connections between these two classes, the corresponding connections within the thalamus and between the thalamus and cortex, the time constants and basic physiology of neurons, and the propagation of activity between neurons. Representing the immense intricacy of brain anatomy and physiology with suitable summary equations and average parameter values has meant that the model is able to capture the essential characteristics of EEG and ERPs, and to do so in a computationally manageable way.

Recent advances in neuroimaging and electromagnetic measurement technology have permitted the exploration of structural and functional brain alterations associated with chronic pain. A number of cortical and subcortical brain regions have been found to be involved in the experience of chronic pain (Baliki et al., 2008; Jensen et al., 2010). Evidence suggests that living with chronic pain shapes the brain from both an architectural and a functional perspective, and that individuals living with chronic pain display altered brainwave activity even at rest. Quantitative EEG (qEEG) is a method of spectral analysis that utilizes a fast Fourier transform algorithm to convert analog EEG signals into digital signals, allowing for precise quantification and analysis of signals both at single electrode locations and across the scalp as a whole. An important advance that has been permitted by qEEG analysis is the development of lifespan normative databases against which individual qEEGs can be compared (Kaiser, 2006; Thatcher et al, 2000). Pilot data utilizing qEEG to examine brainwave patterns of individuals with chronic pain have revealed altered EEG activity at rest compared to age- and gender-matched healthy individuals (Burroughs, 2011). The current investigation extended the findings of the pilot study by utilizing qEEG to examine a larger sample of individuals with chronic pain. Individuals with chronic pain displayed significantly reduced slow wave activity in frontal, central, and temporal regions. Findings will be presented in terms of specific patterns of altered EEG activity seen in individuals with chronic pain.

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.

The single task condition of the Urbanczyk and Kennelly (1991) study was conducted while recording a continuous electroencephalographic (EEG) record. Attention was allocated by instructed lateral head orienting and eye gaze either congruently or incongruently with lateralized cognitive tasks. Thirty university subjects retained a digit span or a spatial location span for a 20 second retention interval. EEG data were extracted from the 20 second retention intervals and interhemispheric coherence was calculated for homologous sites in the temporal, parietal and occipital regions of the brain. There was a main effect for group, with congruent orienting producing greater coherence values than incongruent orienting. This effect of attention on alpha coherence values was found in the low alpha (8-10 Hz) frequency band. This provides evidence that the lower alpha frequency band is reflective of manipulations of attention. The higher coherence measures for the congruent orienting group indicates that homologous regions of the two hemispheres are more coupled into a single system when lateralized attention activates the same hemisphere performing the cognitive task. In the higher alpha frequency band (11-13 Hz) group, sex, site and task interacted. This provides evidence that the higher alpha band is more affected by cognitive processing of the specific task undertaken. An interhemispheric brain system, affected by the lateral orientation of attention, may underlie psychometric intelligence's general “g” ability (Spearman, 1927.)

The brain is a highly complex network of nonlinear systems with internal dynamic states that are not easily quantified. As a result, it is essential to understand the properties of the connectivity network linking disparate parts of the brain used in complex cognitive processes, such as working memory. Working memory is the system in control of temporary retention and online organization of thoughts for successful goal directed behavior. Individuals exhibit a typically small capacity limit on the number of items that can be simultaneously retained in working memory. To modify network connections and thereby augment working memory capacity, researchers have targeted brain areas using a variety of noninvasive stimulation interventions. However, few existing methods take advantage of the brain's own structure to actively generate and entrain internal oscillatory modulations in locations deep within the auditory pathways. One technique is known as binaural beats, which arises from the brain's interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. The mismatch between these tones is perceived as a so-called beat frequency which can be used to modulate behavioral performance and cortical connectivity. Currently, all binaural stimulation therapeutic systems are open-loop "one-size-fits-all" approaches. However, these methods can prove not as effective because each person's brain responds slightly differently to exogenous stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI), which causes cognitive impairments beyond those expected based on age. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, I will present a comparison of algorithms to determine brain connectivity, results of open-loop based binaural stimulation, the development of a closed-loop brain network simulation platform, and finally an experimental study to determine the effectiveness of closed-loop control to modulate brain networks hence influencing cognitive abilities.
Ph. D.

In this work, we present a set of methods aimed at improving the discriminative power of time-varying features of signals that contain noise. These methods use properties of noise signals as well as information theoretic techniques to factor types of noise and support signal inference for electroencephalographic (EEG) based brain-machine interfaces (BMI). EEG data were collected over two studies aimed at addressing Psychophysiological issues involving symmetry and mental rotation processing. The Psychophysiological data gathered in the mental rotation study also tested the feasibility of using dissociations of mental rotation tasks correlated with rotation angle in a BMI. We show the feasibility of mental rotation for BMI by showing comparable bitrates and recognition accuracy to state-of-the-art BMIs. The conclusion is that by using the feature selection methods introduced in this work to dissociate mental rotation tasks, we produce bitrates and recognition rates comparable to current BMIs.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.
Committee Member: Alexander Gray; Committee Member: Charles Lee Isbell Jr.; Committee Member: Melody Moore Jackson; Committee Member: Paul M. Corballis; Committee Member: Thad Starner. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Frontal electroencephalographic (EEG) alpha (α) asymmetry may index the activation of lateralized affect and motivation systems in humans. Resting EEG activation was measured and its relationship to Iowa gambling task (IGT) performance was evaluated. No effects were found for α power asymmetry. However, beta (β) power asymmetry, an alternative measure of resting EEG activation, was associated with the number of risky decisions made in the early portion of the task. Additionally, IGT deck selection patterns suggest there are at least three distinct performance styles in healthy individuals. Interestingly, β power asymmetry contradicts performance predictions based on accepted frontal asymmetry affect and motivation models.

O EEG é um exame comumentemente utilizado para diagnóstico de patologias como a epilepsia ou distúrbios do sono. Ele costuma ser apresentado e interpretado por intermédio da avaliação - em geral, visual - da representação da frequência e amplitude da atividade elétrica global do encéfalo ao longo do tempo. A avaliação deste exame pode ir além desta análise do sinal de onda e sua descrição fenomenológica. Outras formas de representação dos dados de EEG são possíveis e investigadas neste trabalho. O objetivo deste foi o desenvolvimento de um aplicativo que permitisse visualizar os dados de EEG e de uma abordagem de coerência de EEG - denominada PDC - em dispositivos móveis. Este programa pretendeu dar mobilidade ao profissional de saúde e servir também como ferramenta de ensino e aprendizagem. Revisões sistemáticas da literatura mostraram a viabilidade de tal desenvolvimento. Há diversas abordagens na literatura, porém, não foi localizado nenhum estudo mais profundo quanto a eficácia das ferramentas disponíveis. Pretendeu-se corrigir tal carência propondo-se uma metodologia de avaliação sistemática com o auxílio de distintos usuários com diferentes níveis de habilidade em análise de EEG. Isto permitirá introduzir critérios objetivos para verificar a viabilidade da ferramenta proposta bem como permitirá estabelecer parâmetros de comparação entre diferentes propostas.
An electroencephalogram (EEG) is a test that measures and records the global electrical activity of brain. It\'s commonly used for diseases diagnosis, such as epilepsy and sleep disorders. It\'s often displayed and interpreted observing - visually, nearly always - the waveform representation of brain electrical activity. That interpretation may go one step further signal waveform analysis and its phenomenological description. Alternative ways are possible and are investigated here. The present work deals with the development of an mobile application to show EEG data and an EEG coherence approach - called PDC. It intends provide a mobility option to healthcare professionals and be used as teaching and learning tool. Systematic reviews have shown the feasibility of such development. There are several approaches to similar applications, in the literature. However, was not found any study on the effectiveness of the available tools. We developed a methodology proposal for the systematic evaluation to fill this gap. It will be performed with the aid of different users with distinct skill levels in EEG analysis. This will introduce objective criteria to verify the proposed tool practicability and establish parameters for comparing different proposals.

Australian sheep routinely undergo surgical procedures without pain relief for castration, tail docking and mulesing. Electroencephalography (EEG) measures pain in livestock by comparing pre- and post-surgical states. This thesis aimed to assess EEG as an objective measure of pain and analgesic efficacy in sheep undergoing surgical procedures in a commercial farm setting. EEG output of anaesthetised lambs was compared to conscious lambs undergoing castration. Significant changes were found in conscious lambs between lignocaine treated and untreated lambs from pre- to post-castration. A pilot study examined EEG of conscious lambs undergoing surgical or rubber ring castration and tail docking with or without pre- or post-operative anaesthesia. Significant changes were found for all treatments compared to baseline regardless of analgesia. A further study assessed EEG examining analgesic efficacy in lambs undergoing mulesing. Lambs were treated with either xylazine, Tri-Solfen®, both or neither. Consistent decreases in EEG power across all treatments suggests no treatment effectively ameliorated EEG response to mulesing. Behaviour, eye temperature and EEG were assessed in lambs undergoing castration with lignocaine, meloxicam or sham. No meaningful treatment differences were observed as sham lambs had similar EEG trends to castrated lambs. Behavioural changes did not differentiate treated and untreated castrated lambs but did distinguish sham lambs due to significantly fewer pain behaviours observed. The results from this thesis indicate current pain mitigation strategies are not sufficient. EEG is limited as an objective measure of analgesic efficacy in conscious lambs as response may be confounded by non-painful factors. Use of behavioural and physiological parameters, in conjunction with EEG or alone, offer non-invasive methods of pain and analgesic assessment in sheep.

Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.

Investigators have shown that monitoring the electrical activity of the brain can indicate CNS status and that it can enhance the assessment of anesthetic depth when used with other clinical signs. It is important to understand the variables that are produced by EEG monitors and used to assess CNS status and to understand similarities and differences between stages of intra-operative sleep. This investigation used well studied stages of normal sleep for comparison with different stages of intra-operativesleep. EEG data from 6 intra-operative from 6 intra-operative and 6 normal sleep subjects were collected on FM recorder and processed with 2 clinical EEG monitors. The results failed to show any similarities in EEG variables between stages of normal sleep and intra-operative sleep. Comparison of the two monitors in assessing similar EEG waveforms showed that they have different sensitivities to frequency and amplitude and they produce different results with differences in their ability to separate information.

Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability.
Master of Science
Brain-Computer Interfaces (BCIs) as the name suggests allows individuals to interact with computers using electrical activity captured from different regions of the brain. These devices have been shown to allows subjects to control a number of devices such as quad-copters, robotic arms, and computer cursors. Applications such as these obtain electrical signals from the brain using electrodes either placed non-invasively on the scalp (also known as an electroencephalographic signal, EEG) or invasively on the surface of the brain (Electrocorticographic signal, ECoG). Before a participant can effectively communicate with the computer, the computer is calibrated to recognize different signals by collecting data from the subject and learning to distinguish them using a classification algorithm. In this work, we were interested in analyzing the effectiveness of using signals obtained from deep brain structures by using electrodes place invasively (also known as intracranial EEG, iEEG). We collected iEEG data during a two hand movement task and manually analyzed the data to find regions of the brain that are most effective in allowing us to distinguish signals during movements. We later showed that this task could be automated by using classification algorithms that are borrowed from electroencephalographic (EEG) signal experiments.

Medical image and signal analysis play a vital role in biomedical research where a variety of computational methods are available. In recent years, the methods were widely variable across studies which was possibly the reason for conflicting results in many studies. Moreover, these differences can hinder the development of multi-dataset federated machine learning (ML) and multimodal studies. Therefore, to characterize how methodological differences can influence the results, this thesis conducted two case studies of contrasting workflows in electroencephalography (EEG) signals and Magnetic resonance imaging (MRI) analysis. In the first experiment, EEG preprocessing methods, that previously varied across studies and produced inconsistent results, were assessed. The results indicated that several choices can modulate the results significantly. The second experiment considered clinical dementia rating (CDR) classification pipelines using MRI. Previously reported models lacked the ability to classify all five levels of CDR, had limited interpretability and used restricted data sources making the models biased towards the experimental settings. Furthermore, most of the reported pipelines were found to have “data leakage”, a phenomenon which resulted in highly accurate models but with low repeatability in new datasets. To overcome these limitations, a deep learning model with improved interpretability was developed. The results indicated that MRI experimental settings can substantially affect the model accuracy invalidating real-world use cases for previously published models. The two experiments signify that disproportional or biased results are obtained across studies if methods or experimental settings vary. Therefore, a common set of agreed standards for analysis is needed. This will allow comparison of studies, integration of information sources such as combined EEG and MRI, and pave the way for multi-dataset studies such as those being proposed using federated ML.

Master’s thesis deal about electroencefalography, measurement EEG signals and analysis measuermed signals. Project contains two basis practical parts. Firts part contain two PC’s programs that’s are used to fundamental analysis to frequence-domain and visual display of brain mapping created with Matlab. Second chapter of practical parts includes two PC’s programs created with LabView. First of them is the EEG biofeedback making use for advanced analyses and second program is used to detection segment of stacionarity.

This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and statistical processing.

Although the mu rhythm is traditionally defined as a normal EEG variant, recent evidence suggests that mu may have functional significance in a variety of disorders such as autism, Parkinson's disease, and multiple sclerosis. While an increasing number of articles have focused on the blocking mechanism of mu in relation to various cognitive processes and disorders, few have examined the significance of a prominent mu rhythm in the background EEG. A few studies have examined the relationship between the mu rhythm and psychological disturbance, such as attentional and affective disorders. Increasing evidence suggests that EEG and qEEG variables may be useful in classifying psychiatric disorders, presenting a neurophysiological alternative to traditional symptom-based diagnosis and classification. Thus, the intention of the present study was to examine the relationship between neuropsychological variables, gathered from multiple assessment sources, and the presence of a prominent mu rhythm in the EEG. Results did not show a statistically significant difference between individuals with and without a prominent mu rhythm on the Test of Variables of Attention (TOVA); although individuals in the mu group showed a pattern of increased impulsivity and performance decrement over time. For adults, no significant differences were observed between groups on psychological variables measured by the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). However, for children, the mu and control groups differed on several behavioral and emotional variables on the Child Behavior Checklist (CBCL). Results are examined in the context of other research and clinical implications are discussed.

This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.

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.

Bonifácio, P.R.C. Estudo da sincronização e dessincronização cortical em EEG associada a movimento de membros inferiores. 84 f. Dissertação (Mestrado) – Escola Politécnica, Universidade de São Paulo, São Paulo, 2006. Quando o ser humano prepara a execução de um movimento conhecido e treinado, é possível identificar as fases desta preparação no sinal eletrencefalográfico (EEG), sobre as faixas de freqüência delta, mu, beta e gama. A preparação do movimento dos membros inferiores pode ser antecipada em milhares de milissegundos e a facilitação descendente no sistema nervoso central pode ser identificada. Este trabalho sistematiza um processo de aquisição para o sinal de preparação motora baseado no EEG, em área associada a membros inferiores, como subsídio à identificação de Sincronização e Dessincronização corticais, nas faixas de freqüência acima de 13Hz, como reflexo da interação funcional de alças córtico-talâmicas, para movimento conhecido e treinado, em tarefa de retardo instruído. Buscou-se comprovar a possibilidade de uso de um número reduzido de cinco canais de EEG para monitorar esta preparação cortical, bem como formalizar a possibilidade de usar o sinal processado nas faixas beta e gama. Foram obtidos resultados coerentes com a literatura, com dessincronização mu e beta com todos os sujeitos e sincronização gama evidenciada em metade dos sujeitos. Como resultado principal ficou evidenciado que: o uso de cinco canais suportando a coleta de EEG apresenta-se cabível e possui uma boa capacidade de discriminação dos fenômenos de ERS/ERD nas faixas de interesse, para o monitoramento da atividade cortical pré-movimento para membros inferiores; que é possível identificar, no paradigma empregado, os períodos de envio de informação para os tratos descendentes, e quais são as condições mínimas para realizar o monitoramento com a preparação ambiental adequada para evitar os distratores mais conhecidos.
The analysis of the electroencephalogram (EEG) enables the identification of a pre-movement activity associated with the execution of a known and pre-trained movement. The main frequency bands to achieve this identification are the delta, mu, beta and gamma. The initiation of the movement of the legs can be anticipated by thousands of milliseconds by a suitable analysis of the EEG. The objective of this work is to develop signal acquisition and signal processing methodologies associated with the scalp EEG during pre-movement trials. The EEG recordings are concentrated over the leg cortical area with the objective of identifying cortical synchronization and desynchronization (ERS/ERD) associated with trained movements. The number of available EEG channels was limited to five and one task was to investigate if this low number of channels would be enough for the purposes of monitoring cortical preparation. The results were consistent with those presented in the literature. In all subjects mu and beta desynchronization were observed and in half (four) of them the gamma band showed synchronization. One conclusion was that the cortical ERS/ERD associated with the lower limbs are recognizable using only five EEG channels. Several aspects of the experimental paradigm and the signal processing were adjusted for optimal results. members, foot, leg.

This master’s thesis deals with issues of the detection of K-complexes in EEG sleep signals. Record from an electroencephalograph is important for non-invasive diagnosis and research of brain activity. The scanned signal is used to examine sleep phases, disturbances, states of consciousness and the effects of various substances. This work follows the automatic detection of K-complexes, because the manual labeling of graphoelements is complicated. Two approaches were used –Stockwell transform and bandpass filtration followed by TKEO operator application. All algorithms were created in the MATLAB R2014a.