An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals

Arrigo Palumbo, Nicola Ielpo, Barbara Calabrese
<span title="2022-01-01">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded
more &raquo; ... CI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22010318">doi:10.3390/s22010318</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35009860">pmid:35009860</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8749705/">pmcid:PMC8749705</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iosto2ixvndolgi76nfe7ylcw4">fatcat:iosto2ixvndolgi76nfe7ylcw4</a> </span>
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