EEG-Based Brain-Computer Interface Implementation

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dc.contributor.advisorSütő, József
dc.contributor.authorSuresh Kumar, Rahul
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-02-22T22:58:14Z
dc.date.available2025-02-22T22:58:14Z
dc.date.created2024
dc.description.abstractMany research studies have investigated the utilization of different stimuli in EEG-based machine learning studies. Prior research on music stimuli has shown that music has a strong effect on brainwaves, which can be detected through EEG signals without the need for participant input. In this study, we expand on this investigation by using the 16-channel CytonDaisy Chain sensor from OpenBCI to create a basic artificial neural network. We examine the EEG signals recorded by the CytonDaisy Chain sensor, discussing the conditions of data collection, the performance of the neural network with diverse hyperparameters, and the impact of different songs on different participants. Additionally, we conducted further testing on data collected from a different day for a subset of subjects. Furthermore, we evaluate the OpenBCI sensor's performance in comparison to the EMOTIV EPOC+ employed in prior research.
dc.description.courseMérnökinformatikus
dc.description.degreeBSc/BA
dc.format.extent56
dc.identifier.urihttps://hdl.handle.net/2437/387481
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectArtificial neural network
dc.subjectDigital filtering
dc.subjectOpenBCI Cyton
dc.subjectfeature engineering
dc.subjectMusic stimuli
dc.subject.dspaceInformatics
dc.titleEEG-Based Brain-Computer Interface Implementation
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