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Electroencephalography
Jarošová, Veronika ; Ronzhina, Marina (referee) ; Chmelař, Milan (advisor)
This semestral paper has its aim to describe the electroencephalography. The beginning of paper is devoted to the history of electroencephalography, then there is both spontaneous and evoked brain activity described. Further, the paper deal with the electroencephalography. At the conclusion of teoretical part is described the methodology and processing of electroencephalography signal. The practical part of the work is focused on the creation of three various exercises on the epilepsy theme, reflexes and memory. Then the exercises were verified practically at fourteen volunteers. Further, in the work there is mentioned the instruction for particular measures, valuation results and its discussion.
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Biofeedback and its practical use
Dvořák, Jiří ; Hrozek, Jan (referee) ; Čmiel, Vratislav (advisor)
The aim of this work is describe common methods of biological feedback therapy that is used to treat some psychosomatic diseases. Subsequently, the description is focused on minimal brain dysfunction treatment by the help of EEG biofeedback. Properties and technical requirements for this therapy are concretized. The last part of this thesis is dedicated to the design and realization of practical software tool for EEG biofeedback therapy which is made in LabView 7.1. The M535 acquisition unit and NI USB-6221 measuring device are used for hardware solution.
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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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Person Identification and Verification Using EEG
Žitný, Roland ; Orság, Filip (referee) ; Tinka, Jan (advisor)
The aim of this work was to create a brain-computer interface that reliably identifies and verifies a person using his electroencephalographic signals. Creating a user profile and verifying it is based on processing reactions to his own face, and the face of strangers or acquaintances. Algorithms such as bandpass and noise removal using wavelet transformation are user to filter signals. The classification of reactions is performed using a convolutional neural network or linear discriminant analysis. The average accuracy of the linear discriminant analysis is 66.2 % and of the convolutional neural network is 58.7 %. The maximum achieved accuracy was with linear discriminant analysis and at 93.7 %.
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Identification of the parameters of an electroencephalographic recording system
Svozilová, Veronika ; Sekora, Jiří (referee) ; Mézl, Martin (advisor)
Elektroencefalografický záznamový systém slouží k vyšetření mozkové aktivity. Na základě tohoto vyšetření lze stanovit diagnózu některých nemocí, například epilepsie. Účelem této práce bylo zpracování signálu z toho systému a vytvoření modelového signálu, který bude s reálným signálem porovnán. Uměle vytvořený signál vychází z Jansenova matematického modelu, který byl dále implementován v prostředí MATLAB a rozšířen ze základního modelu na komplexnější zahrnující nelinearity a model rozhraní elektroda – elektrolyt. Dále bylo provedeno měření signálů na EEG fantomu a následná identifikace parametrů naměřených signálu. V první fázi byly testovány jednoduché signály. Identifikace parametrů těchto signálů sloužila k validaci daného EEG fantomu. V druhé fázi bylo přistoupeno k testování EEG signálů navržených podle matematického Jansenova modelu. Analýza veškerých signálů zahrnuje mimo jiné časově frekvenční analýzu či ověření platnosti principu superpozice.
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Evoked potentials and its sensing
Fabianová, Katarína ; Janoušek, Oto (referee) ; Chmelař, Milan (advisor)
An electrical activity of the brain can be spontaneous (a signal is produced during the life of the individual) or can be evoked (a reply to the external above threshold stimulus). This work is largely dedicated to evoked potentials by which the brain reacts normal or abnormal to given external stimuli (sound, light, etc.). The main essence of this work is a project of the simple acoustical generator for the research of evoked potentials. This acoustical generator is also useable in connection with the EEG device from Alien Technic without any intervention to this device. Acoustical evoked potentials belong to the most widely used objective examinations of the sense of hearing.
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Electric brain acticity changes during targeted emotional stimulation evaluated using sLORETA method
Špidlenová, Lada ; Pánek, David (advisor) ; Pavlů, Dagmar (referee)
Name: Electrical cerebral activity changes during targeted emotional stimulation, evaluated by sLORETA Goals: The main goal of this thesis is the comparison of electrical brain activity changes during targeted emotional stimulation. Method: The research was conducted by measuring electrical cerebral activity using Natus Neurology 32-channel Nicolet™EEG Wireless Amplifier 32/64 device. The activity measurements were taken by an EEG Electro-cap with a total of 19 flat registration electrodes (Fp1, Fp2, F7, F3, Fz, F8, T5, T3, C3, Cz, C4, T4, T6, P3, Pz, 36 P4, O1, O2), placed in accord with the internationally used 10-20 system. The impendance resistance did not exceed 10 kΩ, with a sampling frequency of 512 Hz and a bandpass of 0,5-70 Hz. Resulting data was then processed using NeuroGuide programme. A 30s long, zero-artifact EEG section was then chosen to be exported to sLORETA programme. Probands were divided randomly into three research groups - positive, neutral and negative. Each proband was asked to pass the VVIQ imagination vividity questionnaire. In the next phase, each participant was asked to undergo a three week long imagination training programme, consisting of watching VR videos and targeted imaginations of walking, three times a week. While in the VR simulation, music and sound effect...
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Source analysis and comparation of Feldenkrais inspired movement and visual stimulation using sLORETA
Novotná, Tereza ; Pánek, David (advisor) ; Pavlů, Dagmar (referee)
Title of thesis: Source analysis and comparation of Feldenkrais inspired movement and visual stimulation using sLORETA Objectives: The thesis aim is to evaluate intracerebral source activity during a simple arm movement inspired by Feldenkrais method and to compare it with a visual stimulation of the same movement presented in a clip and with an imagination of the same movement. The movement inspired by Feldenkrais method was simplified to a repeated flexion of the dominant arm. Source analysis was evaluated from EEG and processed using sLORETA program, Methods: To obtain the data, experimental group was put together containing 12 participants aged 22-60, (mean = 27.2), both genders included. Participants were subjected to one-off measurement by the EEG instrument. Feldenkrais inspired movement of a flexion of a dominant upper right arm was investigated. The experiment constisted of six parts: 1. native EEG record with eyes closed and open, 2. active flexion of the dominant upper arm with eyes closed, 3. active flexion of the dominant upper arm with eyes opened, 4. watching video presenting repeated upper arm flexion, 5. dominant upper arm flexion imagination with eyes closed. Every part lasted for two minutes. Between individuals parts was inserted a pause. Obtained EEG data were processed with...
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Person Identification and Verification Using EEG
Žitný, Roland ; Orság, Filip (referee) ; Tinka, Jan (advisor)
The aim of this work was to create a brain-computer interface that reliably identifies and verifies a person using his electroencephalographic signals. Creating a user profile and verifying it is based on processing reactions to his own face, and the face of strangers or acquaintances. Algorithms such as bandpass and noise removal using wavelet transformation are user to filter signals. The classification of reactions is performed using a convolutional neural network or linear discriminant analysis. The average accuracy of the linear discriminant analysis is 66.2 % and of the convolutional neural network is 58.7 %. The maximum achieved accuracy was with linear discriminant analysis and at 93.7 %.
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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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