摘要：

基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)由于个体差异和非目标刺激干扰而面临分类性能瓶颈, 且现有方法尚未深入探究视觉分散干扰与个体差异之间的量化关系。为此, 提出一种视觉分散自适应补偿的SSVEP多域协同解码算法, 其核心包含视觉分散自适应标签平滑技术与多域联合解码模型两部分。首先, 基于视觉拥挤效应理论, 构建信号幅值与标签噪声的自适应量化模型, 通过动态调节标签平滑强度实现个体视觉分散程度的量化表征, 在缓解模型过拟合的同时, 削弱非目标刺激的干扰影响, 抑制个体差异的不利影响; 然后, 提出一种多域联合解码模型, 先通过特征提取框架实现时频空域深度协同, 再引入双向长短时记忆(BiLSTM)网络对时序全局依赖关系进行建模, 形成兼具局部感受野与长程上下文感知能力的复合特征表达。在3个公开SSVEP数据集上以0.5 s和1.0 s两个不同长度的时间窗分别进行验证, 结果表明, 在所有实验设置下, 该算法的平均识别准确率和平均信息传输率相较对比方法都更具优势。消融实验表明, 视觉分散自适应补偿机制在各实验设置下均能发挥作用, 尤其是在短时间窗口下, 准确率最高能提升18百分点, 表明该方法为神经解码中的个体适应性优化与时频特征融合提供了新的思路。

关键词: 脑机接口, 稳态视觉诱发电位, 视觉分散, 标签平滑技术, 多域协同

Abstract:

Brain-Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) show classification performance limitations due to individual differences and interference from non-target stimuli. Moreover, existing methods have not thoroughly explored the quantitative relationship between visual distraction interference and individual differences. To address these issues, we propose an SSVEP multi-domain collaborative decoding algorithm with adaptive compensation for visual distraction. The algorithm includes an adaptive label smoothing technique for visual distraction and a multi-domain joint decoding model. First, based on the theory of the visual crowding effect, an adaptive quantitative model linking signal amplitude and label noise is constructed. By dynamically adjusting the intensity of label smoothing, the model achieves a quantitative representation of individual visual distraction levels, thereby mitigating model overfitting while reducing interference from non-target stimuli and suppressing the adverse effects of individual differences. Subsequently, a multi-domain joint decoding model is proposed. This model first achieves deep collaboration across time-frequency-spatial domains through a feature extraction framework and then introduces a Bidirectional Long Short-Term Memory (BiLSTM) network to model temporal global dependencies, forming a composite feature representation that combines local receptive fields with long-range contextual awareness. Validation on three publicly available SSVEP datasets using time windows of 0.5 s and 1.0 s demonstrates that, under all experimental settings, the proposed algorithm exhibits superior average recognition accuracy and average information transfer rate compared to other state-of-the-art methods. Ablation experiments reveal that the adaptive compensation mechanism for visual distraction is effective across all experimental settings, with accuracy improvements of up to 18 percentage points in short time windows. The findings indicate that the proposed approach provides new insights for optimizing individual adaptability and fusing time-frequency features in neural decoding.

Key words: Brain-Computer Interface (BCI), Steady-State Visual Evoked Potential (SSVEP), visual distraction, label smoothing technique, multi-domain collaboration