Academic literature on the topic 'Electroencephalography (EEG)'

Epileptic discharges are not considered a part of the clinical picture of selective mutism, and electroencephalography is generally not recommended in its work-up. This report describes 6 children with selective mutism who were found to have a history of epilepsy and abnormal interictal or subclinical electroencephalography recordings. Two of them had benign epilepsy of childhood with centro-temporal spikes. The mutism was not related in time to the presence of active seizures. While seizures could be controlled in all children by medications, the mutism resolved only in 1. Although the discharges could be coincidental, they might represent a co-morbidity of selective mutism or even play a role in its pathogenesis. Selective mutism should be listed among the psychiatric disorders that may be associated with electroencephalographic abnormalities. It can probably be regarded as a symptom of a more complicated organic brain disorder.

AbstractEEG is the name commonly used for electroencephalography. EEG is an important test for diagnosing epilepsy. Conventional EEG has relatively low sensitivity in epilepsy, ranging between 25–56%. The combination of wake and sleep records gives a yield of 80% in patients with clinically confirmed epilepsy. Video-EEG is most helpful in determining whether seizures with unusual features are actually epilepsy.

AbstractAn electroencephalogram (EEG), detects changes and abnormalities in the electrical activity of the brain and thus provides a way to dynamically assess brain function. EEG may be used to diagnose and manage a number of clinical conditions such as epilepsy, convulsive and non-convulsive status epilepticus, encephalitis, barbiturate coma, brain death, etc., EEG provides a large amount of information to the anaesthesiologist for routine clinical practice as depth of anaesthesia monitors and detection of sub-clinical seizures; and also for understanding the complex mechanisms of anaesthesia-induced alteration of consciousness. In the initial years, the routine clinical applicability of EEG was hindered by the complexity of the raw EEG signal. However, with technological advancement, several EEG-derived dimensionless indices have been developed that correlate with the depth of the hypnotic component of anaesthesia and are easy to interpret. Similarly, with the development of quantitative EEG tools, the routine use of continuous EEG is ever expanding in the Intensive Care Units. This review, describe various commonly used EEG-based monitors and their clinical applicability in the field of anaesthesia and critical care.

Although the EEG has been in constant clinical use for over 40 years, there have been surprisingly few systematic studies of the pattern of referral and clinical use of electroencephalography in a NHS psychiatric service. In view of the current concern about medical audit and cost effective use of special investigation facilities, it is an opportune time to audit the use of clinical electroencephalography in psychiatry. The current study examines the clinical use of electroencephalography in a district psychiatric service that provides comprehensive care for the population of Dundee (population 180,000).

There is a growing interest in monitoring the processed electroencephalography (p-EEG) as a measure of the delivery of anesthetic agent and the depth of the general anesthesia (GA). Each p-EEG monitor constructs an index that is suitable for GA. Although these monitors have become widely used, but it remains controversial whether they can become the gold standard for anesthesia monitoring like pulse oximeter and electrocardiogram. Whether p-EEG-guided anesthesia can affect perioperative outcomes remains unclear. This narrative review describes the relationship between p-EEG monitoring and perioperative outcome such as postoperative neurocognitive function, intraoperative awareness and mortality. Also, this article describes how and what to look beyond the index of processed electroencephalographic monitors. Abbreviations: GA: General anesthesia; EEG: Electroencephalogram; BIS: Bispectral index; POD: Postoperative delirium; CODA: Cognitive Dysfunction after Anaesthesia; POCD: Postoperative cognitive dysfunction; PACU: Post-anesthetic care unit; POQI-6: Perioperative Quality Initiative-6 Consensus Key words: Anesthesia; Delirium; Electroencephalography; Intraoperative awareness; Mortality Citation: Lee KH. Beyond the index of processed electroencephalography: a narrative review. Anaesth. pain intensive care 2022;27(1):112−118; DOI: 10.35975/apic.v27i1.2128 Received: April 07, 2022; Reviewed: August 16, 2022; Accepted: October 20, 2022

Electroencephalogram (EEG) is used to study the activities of human brain using instrument named electroencephalograph. The usage of EEG is now widened to many fields due to its great temporal resolution and other advantages. In this paper, a literature survey has been carried out to explore and categorize applications that have been invented from EEG. The literature survey is done on works from year 2011 up to the present. Three main research areas have been explored, which are medical applications, brain–computer interface and neuromarketing. In medical applications, EEG is used to detect brain abnormality, such as seizures or brain injury. As for BCI, many applications have been proposed for object control, object recognition, rehabilitation and human assistance. In neuromarketing, EEG is used to recognize consumers’ preference such as their preferable products or movies. This literature review shows that the research on EEG is still growing, and the area of applications are expanding.

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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.

In recent years, technology of brain-computer interface has been developed, and the technology has potential extensibility in combination with ubiquitous environments. In science fiction, an idea that personality is copied to a computational device by scanning brain activity, called mind uploading, ghost dubbing, and so on, has been frequently represented. If the idea becomes realized in a future ubiquitous world, design of highly human-friendly interfaces is expected. In this study, as a step towards realizing the idea, we proposed a method to imitate the mind uploading by using electroencephalography (EEG). We proposed a novel method to extract and digitize an essential feature of the EEG signals by using Hilbert-Huang transform (HHT) and symbolic dynamics analysis. A sequence of symbols was obtained from each of the EEG measurement. Then, we constructed 2nd-order Markov sources from the symbol sequences. Both of the cluster analysis and identification tests by human subjects revealed that the Markov source successfully represented both personal invariants and inter individual differences in EEG signals. In sum, we concluded that the imitated mind uploading can be realized by using EEG signals.

This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

Electroencephalography (EEG) is a non-invasive method to monitor the electrical activity of the brain. There are five main broad frequency bands in the EEG power spectrum: alpha, beta, gamma, delta and theta. Data suggest that EEG-derived delta–beta coupling — indicating related activity in the delta and beta frequency bands — might serve as a marker of emotion regulation.