Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
J Neurosci Methods. 2021 Oct 1;362:109300. doi: 10.1016/j.jneumeth.2021.109300. Epub 2021 Jul 31.
P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability.
In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention.
The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance.
The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms.
The proposed MSFF method is able to improve the performance of P300-based BCIs.
基于 P300 的脑机接口通过识别大脑的电信号来提供无需肌肉活动的通信途径。P300 拼写器是最常用的 BCI 应用之一，因为它非常简单可靠，并且能够达到令人满意的通信性能。然而，与其他 BCI 一样，提高 P300 拼写器的性能以提高其实用性仍然是一个挑战。
在这项研究中，我们提出了一种新的多特征子集模糊融合（MSFF）框架，用于 P300 拼写器来识别用户的拼写意图。该方法包括两部分：1）使用 Lasso 算法进行特征选择和特征划分；2）构建集成 LDA 分类器并对这些分类器进行模糊融合，以识别用户意图。
该框架在三个公共数据集上进行了评估，在 BCI 竞赛 II 数据集 IIb 中经过 4 个 epoch 后平均准确率达到 100%，在 BCI 竞赛 III 数据集 II 中达到 96%，在 BNCI Horizon 数据集达到 98.3%。这表明，所提出的 MSFF 方法可以利用信号的时间信息，有助于提高分类性能。
所提出的 MSFF 方法的性能优于或可与先前报道的机器学习算法相媲美。
所提出的 MSFF 方法能够提高基于 P300 的 BCI 的性能。
