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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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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
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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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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