Topographic sleep EEG changes in the acute and chronic stage of hemispheric stroke.

After stroke, the injured brain undergoes extensive reorganization and reconnection. Sleep may play a role in synaptic plasticity underlying stroke recovery. To test this hypothesis, we investigated topographic sleep electroencephalographic characteristics, as a measure of brain reorganization, in the acute and chronic stages after hemispheric stroke. We studied eight patients with unilateral stroke in the supply territory of the middle cerebral artery and eight matched controls. All subjects underwent a detailed clinical examination including assessment of stroke severity, sleep habits and disturbances, anxiety and depression, and high-density electroencephalogram examination with 128 electrodes during sleep. The recordings were performed within 10 days after stroke in all patients, and in six patients also 3 months later. During sleep, we found higher slow-wave and theta activity over the affected hemisphere in the infarct area in the acute and chronic stage of stroke. Slow-wave, theta activity and spindle frequency range power over the affected hemisphere were lower in comparison to the non-affected side in a peri-infarct area in the patients' group, which persisted over time. Conversely, in wakefulness, only an increase of delta, theta activity and a slowing of alpha activity over the infarct area were found. Sleep slow-wave activity correlated with stroke severity and outcome. Stroke might have differential effects on the generation of delta activity in wakefulness and sleep slow waves (1-8 Hz). Sleep electroencephalogram changes over both the affected and non-affected hemispheres reflect the acute dysfunction caused by stroke and the plastic changes underlying its recovery. Moreover, these changes correlate with stroke severity and outcome.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Left-right information flow in the brain during EEG arousals

In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-thetas(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2plusmn0.5 s (for micro arousals), 4plusmn2.2 s (short arousals) and 6.5plusmn3.6 s (long arousals)

Subdural electroencephalogram analysis for extracting discriminating measures in epileptogenic data

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

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

From lateral to mesial: The quest for a surgical cure for temporal lobe epilepsy

Purpose: A gap of more than a hundred years occurred between the first accounts of mesial temporal sclerosis and recognition of its role in the pathogenesis of psychomotor seizures. This paper reviews how the understanding and surgical treatment of temporal lobe epilepsy developed, particularly from the work of Penfield, Jasper, and their associates at the Montreal Neurological Institute (MNI). Methods: Publications on EEG and surgery for temporal lobe seizures from 1935 to 1953 were reviewed and charts of selected patients operated on at the MNI in the same period were examined. Attention was focused on the evolution of surgical techniques for temporal lobe epilepsy. Results: In the late 1930s, some EEG findings suggested deep-lying disturbances originating in the temporal lobe. However, it took another two decades before the correlation of clinical, neurophysiological, and anatomical findings provided evidence for the involvement of the mesial structures in psychomotor or temporal lobe seizures. From 1949 and onward, Penfield and his associates applied this evidence to extend the surgical resections to include the uncus and the hippocampus. Conclusion: The collaborative work of a team led by Penfield and Jasper at the MNI helped to define the role of neurophysiological studies in epilepsy surgery. As a result, the importance of removing the mesial structures in order to obtain better seizure control in patients with temporal lobe epilepsy became firmly established.

Gamma band oscillations under influence of bromazepam during a sensorimotor integration task: An EEG coherence study

The goal of the present study was to explore the dynamics of the gamma band using the coherence of the quantitative electroencephalography (qEEG) in a sensorimotor integration task and the influence of the neuromodulator bromazepam on the band behavior. Our hypothesis is that the needs of the typewriting task will demand the coupling of different brain areas, and that the gamma band will promote the binding of information. It is also expected that the neuromodulator will modify this coupling. The sample was composed of 39 healthy subjects. We used a randomized double-blind design and divided subjects into three groups: placebo (n = 13), bromazepam 3 mg (n = 13) and bromazepam 6 mg (n = 13). The two-way ANOVA analysis demonstrated a main effect for the factors condition (i.e., C4-CZ electrode pair) and moment (i.e., C3-CZ, C3-C4 and C4-CZ pairs of electrodes). We propose that the gamma band plays an important role in the binding among several brain areas in complex motor tasks and that each hemisphere is influenced in a different manner by the neuromodulator. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

THE EFFECTS OF BROMAZEPAM ON THE PERFORMANCE OF A SENSORY-MOTOR ACTIVITY: AN ELECTROENCEPHALOGRAPHIC STUDY

Aims. To investigate the effects of using bromazepam on the relative power in alpha while performing a typing task. Bearing in mind the particularities of each brain hemisphere, our hypothesis was that measuring the relative power would allow its to investigate the effects of bromazepam oil specific areas of the cortex. More, specifically, we expected to observe different patterns of powers in sensory-motor integration, attention and activation processes. Subjects and methods. The sample was made up of 39 subjects (15 males and 24 females) with a mean age of 30 +/- 10 years. The control (placebo) and experimental (3 mg and 6 mg of bromazepam) groups were trained ill the typing task with a randomised double-blind model. Results. A three-way ANOVA and Scheffe test were used to analyse interactions between the factors condition and moment, and between condition and sector Conclusions. The doses used ill this study facilitated motor performance of the typing task. Ill this study, the use of the drug did not prevent learning of the task, but it did appear to concentrate mental effort on more restricted and specific aspects of typing. It also seemed to influence the rhythm and effectiveness of the operations performed during mechanisms related to the encoding and storage often, information. Likewise, a predominance of activity was observed in the left (dominant) frontal area in the 3 mg bromazepam group, which indicates that this close of the drug affords the subject a greater degree of directionality of cortical activity for planning and performing the task. [REV NEUROL 2009; 49: 295-9]

Good surgical outcome in discordant ictal EEG-MRI unilateral mesial temporal sclerosis patients

Purpose:Video electroencephalography (vEEG) monitoring of patients with unilateral mesial temporal sclerosis (uMTS) may show concordant or discordant seizure onset in relation to magnetic resonance imaging (MRI) evidence of MTS. Contralateral seizure usually leads to an indication of invasive monitoring. Contralateral seizure onset on invasive monitoring may contraindicate surgery. We evaluated long-term outcome after anteromesial temporal lobectomy (AMTL) in a consecutive series of uMTS patients with concordant and discordant vEEG findings, uniformly submitted to AMTL on the MRI evidence of MTS side without invasive monitoring. Methods:We compared surgical outcome of all uMTS patients undergoing vEEG monitoring between January 1999 and April 2005 in our service. Discordant cases were defined by at least one seizure onset contralateral to the MRI evidence of MTS. Good surgical outcome was considered as Engel`s class I. We also evaluated ictal SPECT concordance to ictal EEG and surgical outcome. Results:Fifty-four patients had concordant (C) and 22 had discordant (D) scalp EEG and MRI. Surgical outcome was similar in both groups (C = 74% versus D = 86%). Duration of follow-up was comparable in both groups: C = 56.1 +/- 20.7 months versus D = 59.8 +/- 21.2 months (p = 0.83, nonsignificant). Discordant single-photon emission computed tomography (SPECT) results did not influence surgical outcome. Discussion:Surgical outcome was not influenced by contralateral vEEG seizure onset or contralateral increased flow on ictal SPECT. Although vEEG monitoring should still be performed in these patients, to rule out psychogenic seizures and extratemporal seizure onset, a potentially risky procedure such as invasive monitoring may not only not be indicated in this patient population, but may also lead to patients erroneously being denied surgery.

Uma contribuição ao estudo de sinais de EEG para avaliar estados emocionais e mentais de crianças com autismo na interação com robô móvel

O Transtorno do Espectro do Autismo (TEA) caracteriza-se por uma série de distúrbios cognitivos e neurocomportamentais e sua prevalência mundial é estimada em 1 criança com TEA a cada 160 crianças com típico desenvolvimento (TD). Indivíduos com TEA apresentam dificuldade em interpretar as emoções alheias e em expressar sentimentos. As emoções podem ser associadas à manifestação de sinais fisiológicos, e, dentre eles, os sinais cerebrais têm sido muito abordados. A detecção dos sinais cerebrais de crianças com TEA pode ser benéfica para o esclarecimento de suas emoções e expressões. Atualmente, muitas pesquisas integram a robótica ao tratamento pedagógico do TEA, através da interação com crianças com esse transtorno, estimulando habilidades sociais, como a imitação e a comunicação. A avaliação dos estados mentais de crianças com TEA durante a sua interação com um robô móvel é promissora e assume um aspecto inovador. Assim, os objetivos deste trabalho foram captar sinais cerebrais de crianças com TEA e de crianças com TD, como grupo controle, para o estudo de seus estados emocionais e para avaliar seus estados mentais durante a interação com um robô móvel, e avaliar também a interação dessas crianças com o robô, através de escalas quantitativas. A técnica de registro dos sinais cerebrais escolhida foi a eletroencefalografia (EEG), a qual utiliza eletrodos colocados de forma não invasiva e não dolorosa sobre o couro cabeludo da criança. Os métodos para avaliar a eficiência do uso da robótica nessa interação foram baseados em duas escalas internacionais quantitativas: Escala de Alcance de Metas (do inglês Goal Attainment Scaling - GAS) e Escala de Usabilidade de Sistemas (do inglês System Usability Scale - SUS). Os resultados obtidos mostraram que, pela técnica de EEG, foi possível classificar os estados emocionais de crianças com TD e com TEA e analisar a atividade cerebral durante o início da interação com o robô, através dos ritmos alfa e beta. Com as avaliações GAS e SUS, verificou-se que o robô móvel pode ser considerado uma potencial ferramenta terapêutica para crianças com TEA.

A Wireless EEG Acquisition Platform based on Embedded Systems

This paper proposes a wireless EEG acquisition platform based on Open Multimedia Architecture Platform (OMAP) embedded system. A high-impedance active dry electrode was tested for improving the scalp- electrode interface. It was used the sigma-delta ADS1298 analog-to-digital converter, and developed a “kernelspace” character driver to manage the communications between the converter unit and the OMAP’s ARM core. The acquired EEG signal data is processed by a “userspace” application, which accesses the driver’s memory, saves the data to a SD-card and transmits them through a wireless TCP/IP-socket to a PC. The electrodes were tested through the alpha wave replacement phenomenon. The experimental results presented the expected alpha rhythm (8-13 Hz) reactiveness to the eyes opening task. The driver spends about 725 μs to acquire and store the data samples. The application takes about 244 μs to get the data from the driver and 1.4 ms to save it in the SD-card. A WiFi throughput of 12.8Mbps was measured which results in a transmission time of 5 ms for 512 kb of data. The embedded system consumes about 200 mAh when wireless off and 400 mAh when it is on. The system exhibits a reliable performance to record EEG signals and transmit them wirelessly. Besides the microcontroller-based architectures, the proposed platform demonstrates that powerful ARM processors running embedded operating systems can be programmed with real-time constrains at the kernel level in order to control hardware, while maintaining their parallel processing abilities in high level software applications.