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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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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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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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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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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.
|
|
|
Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
|
|
|
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%
|
|
|
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%.
|
|
|
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.
|
|
|
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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