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• W3153580354 abstract "Abstract Objective. In the last decade, the advent of code-modulated brain-computer interfaces (BCIs) has allowed the implementation of systems with high information transfer rates (ITRs) and increased the possible practicality of such interfaces. In this paper, we evaluate the effect of different numbers of targets in the stimulus display, modulation sequences generators, and signal processing algorithms on the accuracy and ITR of code-modulated BCIs. Approach. We use both real and simulated electroencephalographic (EEG) data, to evaluate these parameters and methods. Then, we compared numerous different setups to assess their performance and identify the best configurations. We also evaluated the dependability of our simulated evaluation approach. Main results. Our results show that Golay, almost perfect, and deBruijn sequence-based visual stimulus modulations provide the best results, significantly outperforming the commonly used m-sequences in all cases. We conclude that artificial neural network processing algorithms offer the best processing pipeline for this type of BCI, achieving a maximum classification accuracy of 94.7% on real EEG data while obtaining a maximum ITR of 127.2 bits min −1 in a simulated 64-target system. Significance. We used a simulated framework that demonstrated previously unattainable flexibility and convenience while staying reasonably realistic. Furthermore, our findings suggest several new considerations which can be used to guide further code-based BCI development." @default.
• W3153580354 created "2021-04-26" @default.
• W3153580354 creator A5002398033 @default.
• W3153580354 creator A5061222621 @default.
• W3153580354 date "2021-05-13" @default.
• W3153580354 modified "2023-10-03" @default.
• W3153580354 title "How to build a fast and accurate code-modulated brain-computer interface" @default.
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• W3153580354 doi "https://doi.org/10.1088/1741-2552/abfaac" @default.
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