{"id":"doi:10.1109/tnsre.2021.3114340","kind":"article","title":"Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis","authors":["Liu B","Chen X","Shi N","Wang Y","Gao S","Gao X"],"author_count":6,"year":2021,"published":"2021-10-01","venue":"IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society","cited_by":95,"doi":"10.1109/tnsre.2021.3114340","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1109/tnsre.2021.3114340","archive_url":"https://space-kitty.com/papers/doi:10.1109/tnsre.2021.3114340","updated":null,"pmid":"34543200","pmcid":null,"openalex_id":null,"pdf_url":null,"abstract":"A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis\" by Liu B, Chen X, Shi N et al., https://doi.org/10.1109/tnsre.2021.3114340, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"brain-computer-interfaces","name":"Brain-Computer Interfaces","domain_slug":"neuroscience"}],"cited_in_dossiers":[]}