Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.

This thesis deals with questions of scanning electric activity of brain,¬ so-called electroencephalograph (undermentioned EEG), methods of audiovisual stimulation (undermentioned AVS) and a data-measurement processing. Theoretical part of the thesis is engaged in a theory of EEG signal creation, history and even in current methods of purchasing and processing of the EEG signal, theory of AVS and a theory of biofeedback. For measuring EEG signal with or without an application of AVS methods has been used EEG diagnostic device by Alien company. Its attributes are described in the thesis as well. For elaboration and analysis has been created a programme aplication EEG_xhroze00.fig which realizes frequency spectrum analysis using Fast Fourier Transform algorithm (FFT) and another programme aplication brain_mapping.fig for mapping activity of brain using designed algorithm.

This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.

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.

This paper addresses the problem of detecting K-complexes in sleep EEG. The study of sleep has become very essential to diagnose the brain disorders and analysis of brain activities. Since Kcomplex can have a wide variety of shapes it is very difficult to detect the K-complexes manually. In this paper, I present an automatic method for K-complexes detection based wavelet transform,TKEO and method for classification using feedforward multilayer neural network designed in Matlab. Detection performance reached the value approx. from 52,9 to 83,6 %.

The objective of this thesis is to design an algorithm used for localization of scalp electrodes in MRI structural data. The algorithm is based on fact that electrodes are visible on visualized head surface. The surface of a head is subdivided into smaller fragments, which are transformed from 3D space into 2D. The electrodes are then located in 2D space by use of registration techniques. The proposed algorithm is able to correctly locate up to 73% EEG electrodes, assuming that the subject has short hair. In case when a subject has long hair, the portion of correctly detected electrodes is 49%. The probability of false detection is 22% when the object is short-haired and 35% when long-haired. The algorithm should facilitate the process of EEG electrodes localization during examinations combining imaging modalities of type EEG and MRI.

En associant les récentes techniques de neuro-imagerie (IRMf et Potentiels Evoqués) à la finesse des analyses syntaxiques des approches typologiques et formelles, cette recherche pluridisciplinaire se penche sur la question de la représentation des structures hiérarchiques qui caractérisent l’unité-phrase à travers les langues. La façon dont le cerveau humain représente, construit et l'esprit comprend les diverses structures de phrase, est en effet une des plus importantes questions qui restent encore largement irrésolues dans l’organisation cérébrale du langage. En nous appuyant sur la diversité des langues dans leur organisation syntaxique de l’unité-phrase, nous avons pu isoler différentes dimensions de cette complexité grâce aux propriétés syntaxiques du français dans la formation des questions, ainsi qu'aux spécificités des articulations Topique-Commentaire en chinois mandarin. Suite à une étude du marquage intonationel de la hiérarchie entre Topique et Commentaire, nous avons pu enregistrer les réponses cérébrales (PE) à ce type de constructions en contexte, et ainsi découvrir l’influence de sa signature prosodique sur son traitement en temps réel. Nos deux études d’IRMf apportent quand à elles un éclairage sur les bases neurales de deux dimensions de la complexité syntaxique de la phrase : sa structure hiérarchique et les transformations structurelles dont elle témoigne en cas de dislocation de ses éléments. La première étude, sur les interrogatives en français, met en lumière les corrélats cérébraux de différents types de movements syntaxiques, la seconde, sur les différents phénomènes topicaux du chinois, révèle les représentations et processus qui sont liés à l’activation par le Topique de l’interface entre l’unité-phrase et le niveau du discours. / Combining fine-grained linguistic analyses — from both typological and formal approaches to syntax — with neuro-imaging techniques (fMRI and ERP), this pluri-disciplinary research aims at investigating experimentally the issue of the hierarchical nature and complexity of the linguistic representation of sentence structure and its processing strategies across languages, specifically focusing on the case of Chinese Topic-Comment articulations and on French Interrogative constructions. The question of how the brain achieves sentence structure representation, building and understanding is often seen as one of the most important and unsolved issues of the neural organization of language. Leveraging on cross-linguistic invariance and variability in sentence hierarchical structure organization and building, we found in Chinese and French two exceptional testing grounds to isolate different syntactic complexity dimensions of the sentence-unit encoding. While the on-line auditory comprehension of sentence hierarchical structure in case of minimal intonational cues is investigated thanks to ERP recordings of Topic-Comment articulations in Chinese, two fMRI studies isolate two different syntactic complexity dimensions, respectively reflecting the sentence’s hierarchy and syntactic transformations. The first study, on French interrogative, seeks to isolate the neural correlates of different syntactic movement types. The second study, on Chinese sentence-discourse interface and Topics types, enables us to distinguish word-order surface complexity factors from syntactic movement transformations.

Within the field of computational linguistics, recent research has made successful advances in integrating word space models with n-gram models. This is of particular interest when a model that encapsulates both semantic and syntactic information is desirable. A potential application for this can be found in the field of psycholinguistics, where the neural response N400 has been found to occur in contexts with semantic incongruities. Previous research has found correlations between cloze probabilities and N400, while more recent research has found correlations between cloze probabilities and language models. This essay attempts to uncover whether or not a more direct connection between integrated models and N400 can be found, hypothesizing that low probabilities elicit strong N400 responses and vice versa. In an EEG experiment, participants read a text manipulated using a language model, and a text left unchanged. Analysis of the results shows that the manipulations to some extent yielded results supporting the hypothesis. Further results are found when analysing responses to the unchanged text. However, no significant correlations between N400 and the computational model are found. Future research should improve the experimental paradigm, so that a larger scale EEG recording can be used to construct a large EEG corpus. / Innom datalingvistikken har tidligere forskning gjort framsteg når det gjelder å kombinere ordromsmodeller og n-grammodeller. Dette er av spesiell interesse når det er ønskelig å ha en modell som fanger både semantisk og syntaktisk informasjon. Et potensielt bruksområde for en slik modell finnes innom psykolingvistikk, der en neural respons som kalles N400 vist seg å oppstå i kontekster med semantisk inkongruens. Tidligere forskning har oppdaget en sterk korrelasjon mellom cloze probabilities og N400, og nylig forskning har funnet korrelasjoner mellom cloze probabilities og sannsynlighetsmodeller fra datalingvistikk. Denne oppgaven har som mål å undersøke hvorvidt en mer direkte kobling mellom slike kombinerte modeller og N400 finnes, med hypotesen at lave sannsynligheter leder til store N400-responser og omvendt. Et antall forsøkspersoner leste en tekst manipulert ved hjelp av en slik modell, og en naturlig tekst, i et EEG-eksperiment. Resultatsanalysen viser at manipuleringene til en viss grad gav resultat som støtter hypotesen. Tilsvarende resultat ble funnet under resultatanalysen av responsene til den naturlige teksten. Ingen signifikante korrelasjoner ble oppdaget mellom N400 og den kombinerte modellen. Forbedringer for videre forskning involverer å blant annet forbedre eksperimentparadigmet slik at en storstilt EEG-inspilling kan gjennomføres for å konstruere en EEG-korpus. / Inom datalingvistiken har tidigare forskning visat lovande resultat vid kombinering av ordrumsmodeller och n-gramsmodeller. Detta är av speciellt intresse när det är önskvärt att ha en modell som fångar både semantisk och syntaktisk information. Ett potensielt användningsområde för en sådan modell finns inom psykolingvistiken, där en neural respons kallad N400 visat sig uppstå i situationer med semantisk inkongruens. Tidigare forskning har upptäckt en stark korrelation mellan cloze probabilities och N400, medan en nyare studie har upptäckt en korrelation mellan cloze probabilities och sannolikhetsmodeller från datalingvistiken. Denna uppsats har som mål att undersöka huruvida en mer direkt koppling mellan sådana kombinerade modeller och N400 finns, med hypotesen att låga sannolikheter leder till stora N400-responser och vice versa. Ett antal försökspersoner läste en text manipulerad med hjälp av en probabilistisk modell, och en naturlig text, i ett EEG-experiment. Resultatsanalysen visar att manipuleringen till viss grad gav resultat som stödjer hypotesen. Motsvarande resultat hittades under resultatanalysen av responserna till den naturliga texten. Inga signifikanta korrelationer blev upptäckta mellan N400 och den kombinerade modellen. Förbättringar för vidare forskning involverar bland annat att förbättra experimentparadigmet så att en storskalig EEG-inspelning kan genomföras för att konstruera en EEG-korpus.

Background: The visual system may process spatial frequency information in a low-to-high, coarse-to-fine sequence. In particular, low and high spatial frequency information may be processed via different pathways during object recognition, with LSF information projected rapidly to frontal areas and HSF processed later in visual ventral areas. In an electroencephalographic study, we examined the time course of information processing for images filtered to contain different ranges of spatial frequencies. Participants viewed either high spatial frequency (HSF), low spatial frequency (LSF), or unfiltered, broadband (BB) images of objects or nonobject textures, classifying them as showing either man-made or natural objects, or nonobjects. Event-related potentials (ERPs) and evoked and total gamma band activity (eGBA and tGBA) recorded using the electroencephalogram were compared for object and nonobject images across the different spatial frequency ranges. Results: The visual P1 showed independent modulations by object and spatial frequency, while for the N1 these factors interacted. The P1 showed more positive amplitudes for objects than nonobjects, and more positive amplitudes for BB than for HSF images, which in turn evoked more positive amplitudes than LSF images. The peak-to-peak N1 showed that the N1 was much reduced for BB non-objects relative to all other images, while HSF and LSF nonobjects still elicited as negative an N1 as objects. In contrast, eGBA was influenced by spatial frequency and not objecthood, while tGBA showed a stronger response to objects than nonobjects. Conclusions: Different pathways are involved in the processing of low and high spatial frequencies during object recognition, as reflected in interactions between objecthood and spatial frequency in the visual N1 component. Total gamma band seems to be related to a late, probably highlevel representational process.