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Data Analysis and Clasification from the Brain Activity Detector
Persich, Alexandr ; Grézl, František (referee) ; Szőke, Igor (advisor)
This thesis describes recording, processing and classifying brain activity which is being captured by a brain-computer interface (BCI) device manufactured by OpenBCI company. Possibility of use of such a device for controlling an application with brain activity, specifically with thinking of left or right hand movement, is discussed. To solve this task methods of signal processing and machine learning are used. As a result a program that is capable of recording, processing and classifying brain activity using an artificial neural network is created. An average accuracy of classification of synthetic data is 99.156%. An average accuracy of classification of real data is 73.71%.
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EEG Biofeedback Human Brain - Computer Interface
Kněžík, Jan ; Kořenek, Jan (referee) ; Marušinec, Jaromír (advisor)
This master thesis dwells on EEGbiofeedback (also called Neurofeedback) interface of human brain and the computer and its concrete realization in Java programming language. This system is founded on the basis of the computer, which is accomplishing biological feedback (biofeedback) and the electroencephalography (EEG) by helping that state's scanning of user's brain is realized. By this way is possible to practise the human brain effectively to achieve better concentration, the elimination of sleeping and learning deficiency. Hereafter is the suggestion of direction control of computer mouse by EEG device incorporated, which makes it possible for the man to regulate the direction of the cursor's movement on the screen by the frequency of brain's oscillation. The motivation for solution of this problem is the effort to help to handicapped people to communicate with surrounding world. The introduction of this paper contains the basic facts about human brain, electroencephalography and EEG biofeedback. The following chapters dwell on the specification of claims to developed application, its suggestion and description of actual realization. The final part relates to the BCI (Brain-Computer Interface) systems and suggestion of computer's control by EEGappliance with evaluation of attained results.
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EEG correlates of egocentric and allocentric distance estimates in virtual environment in humans
Kalinová, Jana ; Vlček, Kamil (advisor) ; Telenský, Petr (referee)
Cognitive processes associated with spatial orientation can use different reference frames: egocentric, centered on observer and allocentric, centered on objects in the environment. In this thesis, we use EEG to investigate the dynamics of brain processes accompanying spatial orientation based on these reference frames. Participants were instructed to estimate distances between objects or themselves and objects located in a virtual circular arena; this task was presented in both 2D and 3D displays. Task-related EEG changes were analyzed using a time-frequency analysis and event-related potential analysis of 128-channel EEG recordings. Through time-frequency analysis we found significant power differences in delta, theta, alpha, beta and gamma bands amongst the control, egocentric and allocentric testing conditions. We noted a decrease in alpha power in occipital and parietal regions, while a significantly stronger decrease was observed for the allocentric condition compared to both egocentric and control conditions. A similar pattern was also detectable for the beta band. We also report an increase in theta and delta power in temporal, fronto-temporal and lateral frontal regions that was significantly stronger for the egocentric condition compared to control and, in some electrodes, even...
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Data Analysis and Clasification from the Brain Activity Detector
Ullrich, Petr ; Šůstek, Martin (referee) ; Szőke, Igor (advisor)
This thesis deals with the issue of recording brain activity, implementation of its processing, analysis and classification. The OpenBCI hardware is used for recording. I have studied and described necessary information about recording brain activity and OpenBCI project. Design for data set, data processing and thoughts classification was created. Created system allows classification based on recorded brain activity. The neural network was used for classfication, but the success of the recognition of designed classes was not high.
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Numerosity in college students
KRATOCHVÍLOVÁ, Dominika
This bachelor thesis focuses on problems of numerosity among university students studying humanities. The aim is to compare mathematical abilities and rough mathematical estimation of people studying humanities. The theoretical part deals with mathematical abilities and their disruption, numerosity and especially the definition of the aproximal numerical system. Electroencephalography or EEG, which is the method by which the data for this research was obtained, is described. The practical part is divided into two parts, when the probands are tested first. It works with a standardized test, which further allows to divide probands into mathematically gifted and non-gifted. The main part of the research is the analysis of acquired data using electroencephalograph (EEG), which enables comparison between the above mentioned target groups. The aim of this thesis is to extend information about numerosity. The research included a total of 8 probands, who were at the age from 19 to 25. The results of this research showed that, in contrast to previous research, when there was significant activity in the parietal part on the left side, significant activity could be seen in the parietal-occipital and occipital regions on both sides (not just the left side). In addition, a correlation was demonstrated in probands who achieved above-average results in the Intelligence Test (IST Structure Test) and the rough mathematical estimate was processed faster. Probands who achieved below-average results in the Intelligence Test had slower processing of rough mathematical estimation. However, it turned out that although probands with above-average mathematical intelligence processed the result sooner, their reaction time was longer.
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Psychophysiological correlates of emotion and memory
Jindrová, Miroslava ; Telenský, Petr (advisor) ; Vlček, Kamil (referee)
The first aim was to determine the baseline psychophysiological correlates in healthy subjects as a first necessary step towards the long-term goal of application the psychophysiological techniques in diagnostics mood and cognitive disorders. The second aim was to establish an easily applicable set of tests for evaluating emotional and memory processes by non-invasive psychophysiological methods. EEG, GSR, and eye-tracking data from 100 participants without any neurological or psychiatric disorders were obtained during watching affective pictures and performing memory tests. The spectral powers were computed for each 500 ms of the stimuli in theta, alpha, beta1, beta2 and beta3 bands in 12 areas of the brain. Lower alpha and higher beta3 power was related to higher emotional intensity. Negative emotions were distinguished in spatio-temporal changes of beta1 power and positive emotions showed higher beta3 power in right temporal region. Memory encoding showed higher alpha power. Lower theta and higher alpha power in central regions and overall increase of beta bands were observed during successful memory retrieval. A summary of spatio-temporal spectral correlates to emotional and memory processes was provided by this work. Key words: Electroencephalography, electrodermal activity, psychophysiology,...
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Numerosity in children with Asperger syndrome
KLEMPÍŘOVÁ, Kateřina
This bachelor thesis focuses on the description of a rough mathematical estimate in children with the diagnosis of Asperger´s Syndrome using cognitive evoked potentials. The theoretical part describes Asperger´s Syndrome, electroencefalography, also known as EEG, and cognitive evoked potentials, abbreviated as ERP that have allowed the acquisition and description of final data. Last but not least, asymbolical mathematics is described, as well as systems participating on rough mathematical estimate, their neuroanatomy, development of mathematical abilities in school children, and numerosity in children with autism. The empirical part describes research methodology the objective of which is to describe the ability of rough mathematical estimate in children suffering from Asperger´s Syndrome. Six subjects, aged 10 to 14 took part in the research. Data of neural type, obtained by an electroencelogram, was processed by the Matlab programme, using the EEGlab toolbox. Both, the final EEG and behavioural analysis included all six subjects. The final analysis has proved, as well as previous research following this topic, significant activity in the parietal area. It has been proved that participants who reached results above the average within the subtest of Figures Within the Stanford Binet Intelligence Test also proved faster processing than participants who reached average results in the Figures subtest. The behavioural analysis may allow a presumption of a relation between symbolical and nonsymbolical mathematics. Results have proven that participants successful above the average in the Figures subtest reach a faster processing, meaning that rough mathematical estimate happens faster.
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Neural correlates of number line representation
JANÍČKOVÁ, Petra
Tato práce se zabývá problematikou mentální reprezentace čísla a číselných řad. Výzkumná část práce je zaměřena na ověření změny gama aktivity v parietálním lobu při úlohách spojených s výpočetními operacemi na číselných řadách v kanonické (levostranné) a nekanonické (pravostranné)podobě. Měřením EEG aktivity během prezentace a řešení daných úloh zachycujeme změny aktivity v parietální oblasti mozku.
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Dectection of brain wakefulness from scalp EEG data with higher order statistics
Semeráková, Nikola ; Ronzhina, Marina (referee) ; Labounek, René (advisor)
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.
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