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Steady-State Visually Evoked Potentials (SSVEPs) based Brain-Computer Interfaces (BCIs) offer broad application prospects and significant research value due to their capacity for multiple output commands and high performance. Effectively leveraging the inherent information of SSVEPs across various dimensions, including time, frequency, and spatial domains, can significantly enhance their recognition performance. Existing research has revealed that different independent harmonic components of SSVEPs contain distinct information and represent unique physiological functions. Consequently, this study proposes a method to enhance the detection performance of SSVEP-based BCIs by utilizing the information from each independent harmonic of SSVEP. This method hypothesizes that for specific users and stimulus conditions, the independent harmonics of SSVEP responses should each possess stable and repeatable waveform and spatial propagation characteristics. This method utilizes Task-Related Component Analysis (TRCA) for the extraction and utilization of these characteristics. By integrating the comprehensive information within SSVEPs and the localized information within each independent harmonic, the proposed method has demonstrated superior performance on public datasets compared to traditional filter bank-based FBCCA and TRCA methods. This research confirms the application potential of analyzing and utilizing each independent harmonic of SSVEP, offering new strategies and perspectives for enhancing the performance of SSVEP-based BCIs.
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