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DC Field | Value | Language |
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dc.date.accessioned | 2024-02-20T07:16:32Z | - |
dc.date.available | 2024-02-20T07:16:32Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Gilford, C. (2024). Localisation of brain activity for SSVEP-based BCIs: an fMRI and EEG study (Master's dissertation), | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/118856 | - |
dc.description | M.Sc.(Melit.) | en_GB |
dc.description.abstract | Brain-computer interface (BCI) systems provide an alternative neural pathway for individuals with neuromuscular disorders, enabling them to control external devices when conventional muscle movements are not feasible. These systems capture the brain’s electrical activity, typically using electroencephalography (EEG), and translate it into commands for external devices. Among various control signal types in BCIs, steady-state visually evoked potentials (SSVEPs) have shown remarkable performance. These are electrical signals generated in the brain in response to repetitive visual stimuli (RVS), showing rhythmic neural oscillations matching the stimulus frequency. To interact with the BCI, the user selects a command by focusing on the corresponding visual stimulus. When the BCI detects the SSVEP response, it executes the corresponding command, enabling device control. Current SSVEP-based BCIs use a single-graphic black-white flickering stimulus to generate the SSVEP response, which is typically recorded from the occipital region of the brain. However, the literature suggests that altering stimulus characteristics such as colour, shape, and texture at different flickering frequencies can enhance the SSVEP response in both occipital and non-occipital brain areas. This study uses functional magnetic resonance imaging (fMRI) and EEG recordings to localise the SSVEP activity and investigate how this varies with different stimulus parameters and flicker frequencies. Additionally, identifying robust SSVEP-related brain activity beyond the occipital region would allow a more practical placement of electrodes, in particular on non-occipital areas. Seven different stimuli parameters were presented to the subjects during separate fMRI and EEG data recording sessions. The recorded data was analysed using the general linear model (GLM), signal-to-noise ratio (SNR) and z-score. As expected, it was found that the highest SSVEP response in the brain is located at the occipital region, however temporal and parietal regions still exhibited significant SSVEP amplitudes when using the random dot stimulus in the low frequency range (7.5 Hz and 10 Hz), and the blue-green stimulus in mid and high frequencies ranges (15 Hz, 20 Hz, 24 Hz). Furthermore, this study concluded that flickering frequencies greater than 24 Hz should be avoided as these do not elicit robust SSVEP signals. These findings aid in developing more comfortable, accurate and stable BCIs through the suitable choice of stimuli frequencies, characteristics and electrode placement. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Neuromuscular diseases | en_GB |
dc.subject | Brain-computer interfaces | en_GB |
dc.subject | Electroencephalography | en_GB |
dc.subject | Brain -- Magnetic resonance imaging | en_GB |
dc.title | Localisation of brain activity for SSVEP-based BCIs : an fMRI and EEG study | en_GB |
dc.type | masterThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Engineering | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Gilford, Cheryl (2024) | - |
Appears in Collections: | Dissertations - FacEng - 2024 |
Files in This Item:
File | Description | Size | Format | |
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2419ENRENR502000012575_1.PDF | 31.94 MB | Adobe PDF | View/Open |
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