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Controlling a Virtual Robot Using a Hybrid Brain-Computer Interface with Visual and Auditory Cues
Prášil, Matěj ; Hrubý, Martin (referee) ; Tinka, Jan (advisor)
This work deals with the control of a virtual robot using a hybrid interface between the brain and a computer in response to visual and auditory evoked potentials, EEG signal analysis and processing. OpenBCI hardware is used for scanning. I studied the methods needed for signal processing and designed applications. The output is two applications, one for controlling a virtual robot and the other for signal processing and classification. The average accuracy of signal classification on real data is low, only 22.35%
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Virtual Robot Control Using EEG
Drla, Michal ; Goldmann, Tomáš (referee) ; Tinka, Jan (advisor)
This bachelor thesis aimed to create an application where is user able to control the virtual robot with an EEG signal. The thesis contains a brief introduction that explains how BCI systems which are using EEG work. This introduction not only explains the basics of EEG analysis but also explains brain biology and shows different signals which are extractable from the brain. This thesis also explains the theory of neural networks which are used to implement the analysis. In implementation are shown scripts that were used to collect data and there is also shown the design of the neural network. Results of testing are good, the neural network was making correct decisions and the user was able to control the virtual robot.
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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
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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Data Analysis and Clasification from the Brain Activity Detector
Jileček, Jan ; Černocký, Jan (referee) ; Szőke, Igor (advisor)
This thesis aims to implement methods for recording EEG data obtained with the neural activity sensor OpenBCI Ultracortex IV headset. It also describes neurofeedback, methods of obtaining data from the motor cortex for further analysis and takes a look at the machine learning algorithms best suited for the presented problem. Multiple training and testing datasets are created, as well as a tool for recording the brain activity of a headset-wearing test subject, which is being visually presented with cognitive challenges on the screen in front of him. A neurofeedback demo app has been developed, presented and later used for calibration of new test subjects. Next part is data analysis, which aims to discriminate the left and right hand movement intention signatures in the brain motor cortex. Multiple classification methods are used and their utility reviewed.
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Wheelchair control using EEG signal classification
Malý, Lukáš ; Sadovský, Petr (referee) ; Žalud, Luděk (advisor)
Tato diplomová práce představuje koncept elektrického invalidního vozíku ovládaného lidskou myslí. Tento koncept je určen pro osoby, které elektrický invalidní vozík nemohou ovládat klasickými způsoby, jakým je například joystick. V práci jsou popsány čtyři hlavní komponenty konceptu: elektroencefalograf, brain-computer interface (rozhraní mozek-počítač), systém sdílené kontroly a samotný elektrický invalidní vozík. V textu je představena použitá metodologie a výsledky provedených experimentů. V závěru jsou nastíněna doporučení pro budoucí vývoj.
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Control of the electric wheelchair using EEG classification
Malý, L.
Electric wheelchairs are some of the most important devices to assist physically handicapped persons. This paper presents the concept of brain controlled electric wheelchair designed for people who are not able to use other interfaces such as a hand joystick, and in particular for patients suffering from amyotrophic lateral sclerosis (ALS). The objective is to control the direction of an electric wheelchair using noninvasive scalp electroencephalogram (EEG).
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Brain-Computer Interface Epoc Emotiv and its Potential Commercial Applications
Vencelides, David ; Bečev, Ondřej (advisor) ; Smutný, Zdeněk (referee)
This work is focused on Brain Computer Interface. Specifically, the device EPOC Emotiv. The first part focuses on the introduction to the topic Brain Computer Interface. Definition of terms, a brief history and ways to measure brain activity. The second part deals with specific BCI products that are available on the consumer market open for sale at a price accessible to the ordinary customer. The third part focuses on the specific BCI product EPOC Emotiv In this part the device is introduced and is being examined. This work deals with what are the device capabilities and limitations. Attention is also given to applications that use it. And at the end there is some theoretical proposal for meditation application in which could be EPOC Emotiv further use.
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Factors influencing usability of nervous control of the computer in the information management
Živkov, Martin ; Brixí, Radim (advisor) ; Kalina, Jaroslav (referee)
The work deals with areas of brain--computer interface (BCI). There is theoretical background described because of the research in the first part. The chapter "Analýza technologie (EEG, EMG)" is conceived generally and clarifies basic theory of EEG and EMG. Chapter "Popis zařízení EPOC neuroheadset" examines specific device used in research especially on the technical and functional side. Section "Analýza praktického využití BCI zařízení Emotiv EPOC neuroheadset" is self-explanatory. The focus of the practical part is influence of factors on the usability of BCI neuroheadset EPOC in the field of information management. These factors have been organized and analyzed. Group of factors connected with humans (physical and psychical) was chosen for the application of the research in which was investigated correlation with the ability to learn how to use neuroheadset EPOC, respectively its BCI element. For the research was used experimental method when a sample of volunteers was tested, undergone questionnaire investigation for acquiring human factors and repeatedly tested for the ability to use BCI element of neuroheadset EPOC. There was found out that the ability to learn how to use BCI correlates with optimism (Pearson's correlation coefficient 0,731 [Pkk] on the level of significance 0,01), stability (|0,648| Pkk on the level of significance 0,01), concentration (|0,638| Pkk on the level of significance 0,01 ), self-efficacy (0,549 Pkk on the level of significance 0,05), spatial perception (0,426 Pkk on the level of significance 0,01) in the research part.
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