How to Build a Faster Code-Modulated Visual Evoked Potential based Brain-Computer Interface: The Impact of Codes Bachelor’s Thesis in Artificial Intelligence

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Brain-computer interface (BCI) speller applications are a way for physically limited people to communicate. The type of BCI in this research will use electroencephalography (EEG) to measure the users’ visually evoked potentials (VEP) to decode what character the user is looking at. These VEPs are induced by presenting flash patterns generated by pseudorandom patterns. To be able to distinguish these flashing patterns from one another, they are required to have low correlation. Generating these flash patterns is not trivial, which is why several types of code sets have been used in earlier research, with m-sequences being used the most. This research aims to find alternatives to this code set which achieve higher Information Transfer Rates (ITRs), or induce less eyestrain. These alternatives are: de Bruijn, Gold and Golay code sets. Additionally, it aims to find the impact modulation has on these metrics. Results are obtained by performing a 320 trial copyspelling task in addition to an eyestrain rating across 10 conditions for 12 participants. Results are analysed using reconvolution and canonical correlation analysis (CCA). Not modulating appeared to perform significantly better than not modulating in ITR (122.0 bits per minute > 111.4 bits per minute, p-value < 0.05). It also caused significantly less eyestrain (not modulated: 3.9 < modulated: 5.5, p < 0.001). No significant differences were found in ITR or eyestrain across the different code sets. The highest performing code set reached an ITR of 138.5 bits per minute averaged over all participants. Higher performance was found by not modulated codes, whilst no differences were found between different types of code sets. Variations in setup and decoding could potentially be limitations and explain differences with other research.
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