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

>Magister Scientiae - MSc / INTRODUCTION: Sustained attention is known to be dysfunctional in psychotic disorders. Sustained attention is the ability to remain focused on a specific time-locked stimulus within a task. We aimed to determine whether there are specific group differences between CON and three psychotic disorders: SCZ, MPD and BPD, then to determine differences between these psychotic disorders. This included differences in behavioural performance and prominent electrophysiological event-related potential (ERP) wave components during cueing and target processing of a visual sustained attention task. Further we aimed to characterize ERP waveform component relationships across and within these groups for demographics, substance use, behavioural performance, and clinical variables, the last limited to the psychotic groups. Lastly, we investigated the effects of prescribed medications on ERP wave components within the psychotic groups. METHODOLOGY: 103 participants (29 schizophrenia (SCZ), 28 bipolar disorder with a history of psychosis (BPD), 21 methamphetamine-induced psychotic disorder (MPD), and 30 controls (CON)) underwent electroencephalography (EEG) record while completing a visual continuous performance task. Participants were presented with 60 trials with three consecutive S’s, the presentation of the third S required a behavioural response. Prominent ERP waveform components were extracted from cues and target stimulus. Group differences were determined by ANOVA with Bonferroni post-hoc correction or multivariate Kruskal-Wallis test dependent on data distribution. Relationships between ERP wave components were determined appropriate with Spearman’s Rank order correlation analyses. RESULTS: (1) MPD reported higher use of substances compared to CON, SCZ and BPD. SCZ behavioural performance was poorer compared to CON which was shown by their longer response times, reduced accuracy and increased errors of omission. Clinically, MPD was found to have a shorter duration of illness compared to SCZ. Then SCZ was found to have more positive symptoms compared to BPD whereas BPD had more negative symptoms compared to SCZ. For the first cue, wave component differences were found only over the left hemisphere, for P100 amplitude over the frontal cortex, P300 amplitude over the central cortex, and N170 amplitude over the parietal cortex. For the presentation of the second cue, differences noted for all groups were localised to the frontal and central brain regions, for P100 and N170 ERP waveforms. For the target stimulus wave component differences were found over the prefrontal, frontal and parietal brain regions, within CON, SCZ, BPD and MPD. (2) For the first cue, education positively correlated with the N170 left parietal amplitude in CON and P300 right parietal amplitude in MPD. During the second cue, the left parietal N170 latency in SCZ correlated positively with education and the left central P300 latency correlated negatively with education in MPD. The age on the day of testing correlated positively with the target left frontal P300 latency in MPD. For the first cue, substance use positively correlated with the left and right parietal P300 latency and negatively for the right parietal P100 amplitude in SCZ. In MPD, a negative correlation was noted across left and right prefrontal N170 and P300 amplitudes, and positive correlation for the left prefrontal P300 latency in MPD. For the target stimulus, correlations were evident for the left and right parietal N70, N170 amplitudes, P300 latency, the right parietal P100 amplitude and left central P300 latency in SCZ. For the first cue, in SCZ PANSS total score correlated positively with left and right central P300 amplitudes and the left parietal P300 amplitude. For the second cue; in MPD, the PANSS negative symptom score, positively correlated with the P100 and N170 left parietal amplitude, left and right parietal P150 amplitude, left central and right parietal P300 amplitude. For the target, the Hamilton depression rating scale correlated positively with the left and right frontal P300 amplitude in MPD and then negatively with the right parietal P300 amplitude in SCZ. Behavioural performance in CON, positively correlated with the left parietal N70, P100, P150 and N170 amplitude the number of correct responses, and left central N170 amplitude. While the number of impulsive responses correlated negatively with the left parietal N70, P100, P150 and N170 and the left central N170 amplitude of CON. For the second cue, behavioural performance was related to the fronto-parietal relationship across all groups. For the target stimulus, impulsive responses positively correlated with the left parietal N70 latency in SCZ. Overall response time negatively correlated with the right parietal P300 latency for SCZ. (3) Medication was found to affect ERP wave components during the sustained visual attention task. For the first cue FGA’s increased the left central P100 amplitude in both SCZ and BPD and decreased the left parietal P100 amplitude in SCZ only. The use of antipsychotics increased the right parietal N70 and left central P100 amplitudes in BPD, specifically the right prefrontal N170 amplitude was increased with the use of SGA’s. Then clozapine use increased the left frontal P100 amplitude in SCZ. For the second cue, SGA’s decreased the right parietal P150 amplitude in SCZ but in MPD the right parietal P150 amplitude was increased with haloperidol use, and FGA. SGA’s increased the left parietal P300 latency in BPD and sodium valproate decreased the left prefrontal P300 latency. For the target stimulus, SGA’s decreased the right parietal P100, P150 and left parietal P150 amplitudes and increased the left central P300 latency in BPD. CONCLUSION: (1) sustained attentional performance is poorer in SCZ. Our study adds to previous studies showing attention processing deficits in SCZ, are evident during cueing of a sustained attention tasks; (2) substance use was found to slow cognitive processing, education improved executive function and information processing, and symptom severity was associated with dysfunction of prefrontal and frontal cortices; (3) antipsychotic medication was related to improved processing of salient information. These data support the current literature and provide novel insights to the attentional processing deficits during cueing in the psychotic disorders.

Title: Brain Activity Changes During Walking Affected by Cuieng in Parkinson's Disease Patients Viewed by sLORETA Imaging. Objectives: The aim of this diploma thesis is to acertain changes in electrical activity of the brain, evaluated by sLORETA program, between gait affected by visual and auditory cueing and normal comfortable gait in patients with Parkinson's disease. Methods: This experiment was conducted on 11 patients with Parkinson's Disease aged between 60-78 years. The electrical activity of the brain was registred by scalp EEG during the whole experiment. The experiment consisted of 4 parts. In the beginning we measured resting EEG with closed and then opened eyes (5 + 5 minutes), after that we measured EEG during normal comfortable gait on 3 meters long track (3 minutes). Subsequently we measured EEG during walking affected by cueing, again on the 3 meters long track (3 + 3 minutes). The order of visual and auditory cueing was randomized. Visual cueing was mediated by white lines (tapes) stuck to the floor within 50 centimetres of each other. Auditory cueing was mediated by a metronome. The basic rhythm of the metronome was set on the 70 BPM. Next, the data from the experiment were transferred into the sLORETA program, which enabled statistical proccessing and also imaging of the active...

Einleitung: SSEP sind etabliert, um Patienten intraoperativ zu überwachen, wenn sie sich Operationen im zerebrovaskulären System unterziehen. Das EEG ist eine weitere Methode zur neurophysiologischen Überwachung. In dieser Studie wurde die Wertigkeit des simultanen Ableitens von SSEP und EEG Signalen untersucht. Methode: Dreizehn Patienten (7 Frauen, 6 Männer, mittleres Alter 53.5 Jahre), welche sich dem Clipping eines intrakraniellen Aneurysma unterzogen, wurden eingeschlossen. Die SSEP Latenz 1 (Lat1), Latenz 2 (Lat2) und Amplitude (Amp) wurden kontinuierlich gemessen. Verminderung der Amplitude > 50% oder Verlängerungen der Latenzen > 10% gegenüber den Ausgangswerten wurden als signifikante Ereignisse bewertet. Das EEG wurde mittels einer subduralen Grid-Elektrode gemessen. Alpha % (Al%), Alpha-Delta-Ratio (ADR) und Total Power (TP) wurden ausgewertet. Resultate: Circa 9000 Einzelwerte wurden analysiert. Statistisch signifikante Korrelationen traten zwischen Al% und Amp (K=0.5) auf. Dabei zeigten sich die Veränderungen im EEG (Al%) 6 Minuten vor Ereignissen im SSEP (Amp). Statistisch signifikante Korrelationen traten ebenfalls zwischen Al% und Amp-Ereignissen (K=-0.4) auf. In 6/7 Patienten traten die Al%-Änderungen 7 Minuten vor den Amp-Änderungen auf. Noch stärkere Beziehungen ergaben sich zwischen Lat2 und allen EEG Modalitäten, jedoch reichte die Gesamtzahl der Datenpunkte nicht aus, um statistische Signifikanzen herzuleiten. Schlussfolgerung: Dies ist die erste Beschreibung von signifikanten Beziehungen zwischen quantitativem SSEP und EEG während zerebrovaskulären Operationen. Das quantitative EEG hat das Potenzial, frühe ischämische Ereignisse eher zu detektieren als dies mit SSEP möglich ist. / Introduction: SSEP is established for monitoring patients undergoing cerebrovascular procedures. EEG is another means of neurophysiologic monitoring. The value of a simultaneous SSEP and EEG monitoring was investigated. Methods: Thirteen patients (7 women, 6 men; mean 53.5 years) undergoing cerebral aneurysm clipping were included. SSEP Latency 1 (Lat1), Latency 2 (Lat 2) and Amplitude (Amp) were measured. An Amp decrease >50% or prolongation of Lat >10% were considered significant events. Subdural grid electrodes were utilized to measure EEG. Alpha% (Al%), Alpha-Delta Ratio (ADR) and Total Power (TP) were computed. Results: Approximately 9000 values were analyzed. Statistically significant correlations occurred between Al% and Amp (K=-0.5). EEG (Al%) changes occurred 6 minutes prior to SSEP (Amp) changes. Statistically significant correlations between Al% and Amp events (K0-0.4) occurred, with Al% values changing 7 minutes prior to Amp values in 6/7 patients. A stronger relationship was found between Lat 2 and EEG modalities, but there were not enough data points to achieve statistical significance. Conclusion: This is the first description of significant relationships between quantitative SSEP and EEG during cerebrovascular surgery. Quantitative EEG has the potential to detect early ischemic events prior to SSEP.

Wie bereits mehrfach in anderen Studien beschrieben (Falkenstein et al., 1991; Gehring et al., 1993; Holroyd & Coles, 2002) konnte ein signifikanter Unterschied im EEG nach richtigen Antworten im Vergleich zu Fehlern in einem Zeitfenster von -35 bis 108 ms und von 110 bis 450 ms gezeigt werden. Die als Differenz aus den negativsten beziehungsweise positivsten Peaks nach falschen und richtigen Antworten berechnete „error-related negativity“ (ERN) und „error positivity“ (Pe) als wichtigste Komponenten der Fehlerverarbeitung zeigten im Vergleich zu anderen Studien zwar geringere, aber trotzdem vergleichbare Werte. Um den Einfluss von genetischen Unterschieden auf die Fehlerverarbeitung deutlich zu machen, wurde in dieser Studie untersucht, inwieweit Polymorphismen des Dopamintransporters (DAT), der Catechol-O-Methyl-Transferase (COMT) und des Phosphoproteins Stathmin Einfluss auf die Ausprägung der ERN und der Pe nehmen. Bezüglich des DAT-Polymorphismus konnte ein signifikanter Einfluss weder auf die ERN noch auf die Pe nachgewiesen werden. Hier ist zu vermuten, dass der Polymorphismus den Dopaminhaushalt der Basalganglien nicht nennenswert beeinträchtigt und demnach keinen Einfluss auf die Fehlerverarbeitung hat. Im Hinblick auf den COMT – Polymorphismus zeigte sich zwar kein Effekt auf die ERN, bei der Pe konnte man allerdings signifikante Unterschiede zwischen den Gruppen feststellen. Hier zeigten die homozygoten Träger des „val“ – Allels signifikant höhere Pe – Amplituden als die homozygoten Träger des „met“ – Allels. Heterozygote Personen lagen hinsichtlich der Pe – Amplitude zwischen den beiden anderen Gruppen. Dieser Effekt entspricht zwar nicht den Ergebnissen von anderen Studien (Egan et al., 2005; Frank et al., 2007), sollte aber dennoch Gegenstand weiterführender Forschung sein. Der Polymorphismus im Gen des Phosphoproteins Stathmin beeinflusste zwar die Amplitude der Pe nicht, zeigte aber signifikante Auswirkungen auf die ERN, wobei hier Träger des T-Allels signifikant kleinere ERN-Amplituden aufwiesen als Probanden, die dieses Allel nicht trugen. Allerdings wurde deutlich, dass dieser Effekt nach genauerer Analyse nicht stabil gegenüber Veränderungen war. Trotzdem ist davon auszugehen, dass dieser Polymorphismus die Funktion des ACC und damit auch die Fehlerverarbeitung beeinflusst, wodurch die Notwendigkeit weiterer Untersuchungen auf dem Gebiet des Phosphoproteins Stathmin gegeben ist. / As already published in several studies (Falkenstein et al., 1991; Gehring et al., 1993; Holroyd & Coles, 2002) a significant difference after correct answers compared to errors could be shown in EEG in a time window of -35 to 108ms and 110 to 450ms. The error related negativity (ERN) and error positivity (Pe) showed comparable values to other studies. To demonstrate the influence of genetic difference to the error processing, this study researched the influence of dopamine transporter (DAT), catechol-o-methyl transferase (COMT) and Stathmine to ERN and Pe. The DAT polymorphism does not have any significant influence to ERN and Pe. The COMT polymorphism shows no significant influence to ERN, but there is a significant influence to Pe. The genetic polymorphism of Stathmine does not have any impact on the amplitude of Pe but shows significant influence to ERN.

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Activation in the hippocampus (HC) and prefrontal cortex (PFC) is critical to accurately retrieve overlapping sequences. Experiments 1 and 2 tested the hypotheses that activation in and interaction between HC and PFC increases as overlap between sequences increases in a non-spatial task. Experiment 3 tested the hypothesis that theta oscillations are involved in orchestrating interactions between HC and PFC in a spatial task with overlapping elements. In the first two studies, 17 participants (aged 18-34; 11 female) learned sequences consisting of a picture frame, face, and scene. Conditions varied by degree of overlap. Using fMRI, Experiment 1 tested how degree of overlap affected HC and PFC activation. In overlapping sequences, middle and posterior HC were active when predictability of the correct response increased, dorsolateral PFC was active when participants were able to ascertain the correct set of sequences, and ventrolateral PFC was active when inhibition of interfering associations was required. Experiment 2 examined functional connectivity of HC and PFC during disambiguation. Low- and high-overlap conditions were associated with increased connectivity in separate regions at different times indicating that retrieval under the two conditions used different neural networks and strategies. Low-overlap trials were associated with increased connectivity between HC and prefrontal and parietal regions. High-overlap trials showed increased connectivity between lateral PFC and visual areas, indicating that imagery may be necessary for accurate performance. Using EEG recording, Experiment 3 examined theta activity during retrieval of well-learned, overlapping and non-overlapping mazes in 17 participants (aged 18-34, 11 female). Theta activity increased in overlapping mazes during the first of four hallways, suggesting participants were looking ahead to upcoming turns in the maze. Theta activity increased at the beginning and choice point of the third overlapping hallway, possibly in response to interference from the paired, overlapping maze. These studies provide evidence that (1) overlapping associations in non-spatial sequences elicit interactions between hippocampus and lateral prefrontal cortex, (2) increasing the degree of overlap changes the neural processes required to perform the task, and (3) theta power increases in response to increased cognitive demand and maintenance of sequence information needed to differentiate between overlapping spatial routes.

This doctoral thesis outlines several methodological advances in network science aimed towards uncovering rapid, complex interdependencies of electromagnetic brain activity recorded from the Electroencephalogram (EEG). This entails both new analyses and modelling of EEG brain network topologies and a novel approach to analyse rapid dynamics of connectivity. Importantly, we implement these advances to provide novel insights into pathological brain function in Alzheimer's disease. We introduce the concept of hierarchical complexity of network topology, providing both an index to measure it and a model to simulate it. We then show that the topology of functional connectivity estimated from EEG recordings is hierarchically complex, existing in a scale between random and star-like topologies, this is a paradigm shift from the established understanding that complexity arises between random and regular topologies. We go on to consider the density appropriate for binarisation of EEG functional connectivity, a methodological step recommended to produce compact and unbiased networks, in light of its new-found hierarchical complexity. Through simulations and real EEG data, we show the benefit of going beyond often recommended sparse representations to account for a broader range of hierarchy level interactions. After this, we turn our attention to assessing dynamic changes in connectivity. By constructing a unified framework for multivariate signals and graphs, inspired by network science and graph signal processing, we introduce graph-variate signal analysis which allows us to capture rapid fluctuations in connectivity robust to spurious short-term correlations. We define this for three pertinent brain connectivity estimates - Pearson's correlation coefficient, coherence and phase-lag index - and show its benefit over standard dynamic connectivity measures in a range of simulations and real data. Applying these novel methods to EEG datasets of the performance of visual short-term memory binding tasks by familial and sporadic Alzheimer's disease patients, we uncover disorganisation of the topological hierarchy of EEG brain function and abnormalities of transient phase-based activity which paves the way for new interpretations of the disease's affect on brain function. Hierarchical complexity and graph-variate dynamic connectivity are entirely new methods for analysing EEG brain networks. The former provides new interpretations of complexity in static connectivity patterns while the latter enables robust analysis of transient temporal connectivity patterns, both at the frontiers of analysis. Although designed with EEG functional connectivity in mind, we hope these techniques will be picked up in the broader field, having consequences for research into complex networks in general.

This thesis uses one of Brain Computer Interface (BCI) products, NeuroSky headset, to design a prototype model to control window blind by using headset’s single channel electrode. Seven volunteers performed eight different exercises while the signal from the headset was recorded. The dataset was analyzed, and exercises with strongest power spectral density (PSD) were chosen to continue to work with. Matlabs spectrogram function was used to divide the signal in time segments, which were 0.25 seconds. One segment from each of these eight exercises was taken to form different combinations which were later classified.The classification result, while using two of proposed exercises (tasks) was successful with 97.0% accuracy computed by Nearest Neighbor classifier. Still, we continued to investigate if we could use three or four thoughts to create three or four commands. The result presented lower classification accuracy when using either 3 or 4 command thoughts with performance accuracy of 92% and 76% respectively.Thus, two or three exercises can be used for constructing two or three different commands.

The main goal of this paper is to test the applicability of the device for neural computer control on a group of ten volunteers. In the next part of the paper author focuses on Electroencephalography and the conversion of analog neural signals from brain to digital form. Next chapter describes currently on the market available devices, which allow customers direct computer controlling with the usage of bio signal from brain. The device selected for the purposes of this paper (Emotiv Epoc) is more described in detail. The last goal is an attempt to predict the future development of this technology. The paper demonstrates applicability of this device in its current form for everyday work with Microsoft Project and presents users who are able to learn to control a computer with this device in just 980 minutes of training.