# Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

Article · Liu B, Chen X, Shi N, Wang Y, Gao S, Gao X

- Archive id: doi:10.1109/tnsre.2021.3114340
- Published: 2021-10-01
- Venue: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
- DOI: https://doi.org/10.1109/tnsre.2021.3114340
- PMID: 34543200
- Citations: 95
- Source URL: https://doi.org/10.1109/tnsre.2021.3114340
- Provenance: europepmc

## 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 reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [Brain-Computer Interfaces](https://space-kitty.com/topics/brain-computer-interfaces)

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