The Brain-Computer Interface (BCI) enables direct brain-to-device communication, with the Steady-State Visual Evoked Potential (SSVEP) paradigm favored for its stability and high accuracy across various fields. In SSVEP BCI systems, supervised learning models significantly enhance performance over unsupervised models, achieving higher accuracy in less time. However, prolonged data collection can cause user fatigue and even trigger photosensitive epilepsy, creating a negative user experience. Thus, reducing calibration time is crucial. To address this, Cross-Stimulus transfer learning (CSTL) can shorten calibration by utilizing only partial frequencies. Traditional CSTL methods, affected by time-domain impulse response variations, are suitable only for adjacent frequency transfers, limiting their general applicability. We introduce an Empirical Mode Decomposition (EMD) Based Fuzzy Model (EMD-Fuzzy), which employs EMD to extract crucial frequency information and achieves stimulus transfer in the frequency domain through Fast Fourier Transform (FFT) to mitigate time-domain differences. Combined with a Fuzzy Decoder that uses fuzzy logic for representation learning, our approach delivers promising preliminary results in offline tests and state-of-the-art performance. With only 4 frequencies, our method achieved an accuracy of 82.75% (16.30%) and an information transfer rate (ITR) of 186.56 (52.09) bits/min on the 40-target Benchmark dataset. In online tests, our method demonstrates robust efficacy, achieving an averaged accuracy of 86.30% (6.18%) across 7 subjects. This performance underscores the effectiveness of integrating EMD and fuzzy logic into EEG decoding for CSTL and highlights our method's potential in real-time applications where consistent and reliable decoding is crucial.

翻译：脑机接口（BCI）实现了大脑与设备的直接通信，其中稳态视觉诱发电位（SSVEP）范式因其稳定性和高准确性而在多个领域受到青睐。在SSVEP BCI系统中，有监督学习模型相比无监督模型能显著提升性能，在更短时间内实现更高的准确率。然而，长时间的数据采集可能导致用户疲劳，甚至诱发光敏性癫痫，造成不良用户体验。因此，缩短校准时间至关重要。为解决这一问题，跨刺激迁移学习（CSTL）可通过仅利用部分频率来缩短校准时间。传统的CSTL方法受时域脉冲响应变化的影响，仅适用于相邻频率间的迁移，限制了其普适性。本文提出一种基于经验模态分解（EMD）的模糊模型（EMD-Fuzzy），该模型利用EMD提取关键频率信息，并通过快速傅里叶变换（FFT）在频域实现刺激迁移，以减轻时域差异。结合采用模糊逻辑进行表示学习的模糊解码器，我们的方法在离线测试中取得了良好的初步结果和先进的性能。在仅使用4个频率的情况下，我们的方法在40目标基准数据集上达到了82.75%（16.30%）的准确率和186.56（52.09）比特/分钟的信息传输率（ITR）。在线测试中，我们的方法展现出稳健的效能，在7名受试者中平均准确率达到86.30%（6.18%）。这一性能证明了将EMD与模糊逻辑整合用于EEG解码以进行CSTL的有效性，并凸显了我们的方法在对持续可靠解码至关重要的实时应用中的潜力。