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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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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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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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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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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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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 Measuring And Analyzing System
Blažej, Svätopluk
One of the critical steps in the design of Brain-Computer Interface applications based on electroencephalography is to process and analyze signals in real-time, in order to identify the mental state of the user. Understanding of signal processing is vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. The main asset of this project is to create small, reliable and inexpensive system for analysis of brain activity. This paper describes use of microcontrollers for brain waves readings. Electrodes used for this purpose have sensitive and low noise readings.
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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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