Understanding speech in the presence of noise can be difficult, especially when suffering from a hearing loss. This thesis examined behavioural and electrophysiological measures of speech processing with the aim of establishing how they were influenced by hearing loss (internal degradation) and listening condition (external degradation). The hypothesis that more internal and external degradation of a speech signal would result in higher working memory (WM) involvement was investigated in four studies. The behavioural measure of speech recognition consistently decreased with worse hearing, whereas lower WM capacity only resulted in poorer speech recognition when sound were spatially co-located. Electrophysiological data (EEG) recorded during speech processing, revealed that worse hearing was associated with an increase in inhibitory alpha activity (~10 Hz). This indicates that listeners with worse hearing experienced a higher degree of WM involvement during the listening task. When increasing the level of background noise, listeners with poorer hearing exhibited a breakdown in alpha activity, suggesting that these listeners reached a ceiling at which no more WM resources could be released through neural inhibition. Worse hearing was also associated with a reduced ability to selectively attend to one of two simultaneous talkers, brought on by a reduced neural inhibition of the to-be-ignored speech. Increasing the level of background noise reduced the ability to neurally track the to-be-attended speech. That internal and external degradation affected the tracking of ignored and attended speech, respectively, indicates that the two speech streams were neurally processed as independent objects. This thesis demonstrates for the first time that hearing loss causes changes in the induced neural activity during speech processing. In the last paper of the thesis, it is tentatively suggested that neural activity can be utilized from electrodes positioned in the ear canal (EarEEG) for adapting hearing-aid processing to suite the individual listeners and situation. / Att förstå tal i brus kan vara svårt, speciellt när man lider av en hörselnedsättning. Denna avhandling undersöker beteende- och elektrofysiologiska data med föremålet att bestämma hur de påverkas av hörselskada (intern försämring) och lyssningssituation (extern försämring). Hypotesen att båda intern och extern försämring av talsignalen resulterar i mer aktivering av arbetsminnet under bearbetning av talsignaler har undersökts i fyra studier. Beteendedata visade att talförståelse försämrades med större hörselnedsättning, medan lägre arbetsminneskapacitet endast resulterade i sämre talförståelse när ljudkällorna inte var rumsligt sammanfallande. Elektrofysiologiska mätningar (EEG) gjorda under bearbetning av tal, visade at sämre hörsel associerades med högre inhibitorisk alfa-aktivitet (~10 Hz). Detta indikerar att personer med sämre hörsel upplevde en högre involvering av arbetsminnet under lyssningsuppgiften. Då nivån av bakgrundsljud höjdes, visade personer med sämre hörsel ett sammanbrott av alfaaktiviteten, vilket tyder på att de nådde ett tak där ytterligare arbetsminnes-resurser inte kunde frigöras genom neural inhibition. Sämre hörsel var också förknippat med en reducerad förmåga till at fokusera uppmärksamheten på en av två samtidiga talare, förorsakat av en reducerad förmåga till neuralt att undertrycka den störande talsignalen. En ökning av nivån av bakgrundsljud minskade förmågan att inkoda den relevante talsignalen. Att intern och extern försämring påverkade respektive inkodning av störande och relevant tal, indikerar att de två tal-strömma är neuralt behandlas som oavhängiga objekt. Denna avhandling demonstrerar för första gången att hörselskada förorsakar ändringar i den inducerade neurale aktiviteten under bearbetningen av talsignaler. I avhandlingens sista artikel förslås det preliminärt att neural aktivitet kan upptas från elektroder placerade i hörselgången som kan användas till att kontrollera hörapparat signalbehandling. / <p>Funded by the Oticon Foundation. Project number: 11-2757.</p>

Hĺbková mozgová stimulácia (DBS) predstavuje účinnú liečbu pre pacientov s Parkinsonovou chorobou (PD) alebo farmakorezistentnou epilepsiou. Avšak mechanizmy, ktorými znižuje počet záchvatov a zlepšuje pohyb, zostávajú ešte do značnej miery neznáme. Pre lepšie pochopenie a určenie, v ktorých frekvenčných pásmach je zmena najdôležitejšia, boli urobené porovnania medzi vypnutou a zapnutou DBS pomocou korelačnej metódy a indexu fázového posunu. Jedenásť pacientov s PD a naimplantovanými neurostimulátormi z firiem Medtronic a St.Jude Medical bolo predmetom nahraných dát použitých v tejto práci. Výsledky dokazujú, že zmena konektivity počas DBS nastane a zároveň, že najviac ovplyvňuje najvyššie frekvencie ako beta, nízka gama a vysoká gama. Zmeny v týchto frekvenciách, zodpovedné za motorickú aktivitu, sústredenie a spracovanie informácií, sú v súlade s klinickou teóriou o PD. Počas tejto choroby je patologická beta aktivita hypersynchronizovaná a gama aktivita je znížená práve v motorických oblastiach. Ak sa gama aktivita počas zapnutej stimulácie zvyšuje, fyziologický stav pacientov sa čiastočne znovuobnovuje a tým zlepšuje ich hybnosť. Metódy a výsledky tejto práce budú použité pre ďalší výskum pacientov s PD a epilepsiou.

The main objective of this study is to learn about the audiovisual stimulator and to create hardware resolution of stimulation LED glasses and in environment of the program LabView application, which operate this stimulation LED glasses and in the same time create sound of stimulation. Use environment of the program LabView. Application, which is create in environment of the program LabView, enable operate stimulation LED glasses and arrange sound from three source with two different method, which use modern AVS. Application contains a lot of security, informative and agreement components.

Human-robot collaboration (HRC) occurs when people and robots work together in a shared environment. Current robots often use rigid programs unsuitable for HRC. Multimodal robot programming offers an easier way to control robots using inputs like voice and gestures. In this scenario, human commands from different sensors trigger the robot’s actions. However, this data-driven approach has challenges: accurately understanding power dynamics, integrating inputs, and precisely controlling the robot. To address this, we introduce EEG signals to improve robot control, requiring reliable signal processing, feature extraction, and accurate classification using machine learning and deep learning. Existing deep learning models struggle to balance accuracy and efficiency. This thesis focuses on whether dilated convolutional neural networks can improve accuracy and reduce training and reaction times compared to the baseline. After using the Morlet wavelet for EEG feature extraction, in the thesis, an existing convolutional neural network as a benchmark is employed and uses the dilated convolution algorithm for comparison. Accuracy, precision, recall, and time are used to assess the comparison algorithm’s performance. The conclusion is that the dilated convolutional neural network performs better than the baseline in accuracy and time parameters. / Samarbete mellan människa och robot (HRC) inträffar när människor och robotar arbetar tillsammans i en delad miljö. Nuvarande robotar använder ofta rigida program som inte är lämpliga för HRC. Multimodal robotprogrammering erbjuder ett enklare sätt att styra robotar med hjälp av röst och gester. I detta scenario utlöser mänskliga kommandon från olika sensorer robotens handlingar. Dock har denna datadrivna ansats utmaningar: att noggrant förstå kraftdynamik, integrera inmatning och exakt styra roboten. För att hantera detta introducerar vi EEG-signaler för att förbättra robotstyrningen, vilket kräver pålitlig signalbehandling, funktionsextraktion och noggrann klassificering med maskininlärning och djupinlärning. Nuvarande djupinlärningsmodeller har svårt att balansera noggrannhet och effektivitet. Den här artikeln fokuserar på om dilaterade konvolutionella neurala nätverk kan förbättra noggrannheten och minska träningstider och reaktionstider jämfört med baslinjen. Efter att ha använt Morlet-våg för EEG-funktionsutvinning använder artikeln en befintlig konvolutionell neural modell som referens och jämför med dilaterad konvolution för att bedöma prestandan. Noggrannhet, precision, recall och tidsparametrar bedömer jämförelsealgoritmens prestanda. Slutsatsen är att det dilaterade konvolutionella neurala nätverket presterar bättre än baslinjen vad gäller noggrannhet och tidsparametrar.

Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.

The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but information about a stimulus vector can not be extracted. Using ICA decomposition, we are able to obtain information relating to the certain stimulation, but there is the problem in the final interpretation and selection of the right components in a blind search for variability coupled with the experiment. It was found out that although components calculated from the time sequence EEG are independent for each to other, their spectrum shifts are correlated. This spectral dependence was eliminated by PCA / ICA decomposition from vectors of spectrum shifts. For this method, each component brings new information about brain activity. The results of the heuristic approach were compared with the results of the joined analysis based on the relative and absolute power approach from frequency bands of interest. And the similarity between activation maps was founded, especially for the heuristic model and the relative power from the gamma band (20-40Hz).