论文标题
SSVEP识别的一种自适应任务相关组件分析方法
An Adaptive Task-Related Component Analysis Method for SSVEP recognition
论文作者
论文摘要
稳态视觉诱发电位（SSVEP）识别方法配备了从受试者的校准数据中学习，并且它们可以在基于SSVEP的大脑计算机界面（BCIS）中获得额外的高性能，但是如果校准试验不足，它们的性能会大大恶化。这项研究开发了一种从有限的校准数据中学习的新方法，并提出并评估了一种新型的自适应数据驱动的空间滤波方法，以增强SSVEPS检测。从每个刺激中学到的空间过滤器利用了相应的脑电图试验中的时间信息。为了将时间信息引入整体过程中，采用了基于贝叶斯框架的多任务学习方法。将所提出的方法的性能评估为两个公开可用的基准数据集，结果表明，我们的方法的表现优于竞争方法的差距很大。
Steady-state visual evoked potential (SSVEP) recognition methods are equipped with learning from the subject's calibration data, and they can achieve extra high performance in the SSVEP-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This study develops a new method to learn from limited calibration data and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEPs detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, an multitask learning approach, based on the bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperform competing methods by a significant margin.
