Wei Qingguo, Ding Xinjie
IEEE Trans Neural Syst Rehabil Eng. 2023;31:904-916. doi: 10.1109/TNSRE.2023.3236372. Epub 2023 Feb 3.
One major problem limiting the practicality of a brain-computer interface (BCI) is the need for large amount of labeled data to calibrate its classification model. Although the effectiveness of transfer learning (TL) for conquering this problem has been evidenced by many studies, a highly recognized approach has not yet been established. In this paper, we propose a Euclidean alignment (EA)-based intra- and inter-subject common spatial pattern (EA-IISCSP) algorithm for estimating four spatial filters, which aim at exploiting intra- and inter-subject similarities and variability to enhance the robustness of feature signals. Based on the algorithm, a TL-based classification framework was developed for enhancing the performance of motor imagery (MI) BCIs, in which the feature vector extracted by each filter is dimensionally reduced by linear discriminant analysis (LDA) and a support vector machine (SVM) is used for classification. The performance of the proposed algorithm was evaluated on two MI data sets and compared with that of three state-of-the-art TL algorithms. Experimental results showed that the proposed algorithm significantly outperforms these competing algorithms for training trials per class from 15 to 50 and can reduce the amount of training data while maintaining an acceptable accuracy, thus facilitating the practical application of MI-based BCIs.
限制脑机接口（BCI）实用性的一个主要问题是需要大量带标签的数据来校准其分类模型。尽管许多研究已证明迁移学习（TL）在解决这一问题上的有效性，但尚未建立一种得到高度认可的方法。在本文中，我们提出了一种基于欧几里得对齐（EA）的受试者内和受试者间共同空间模式（EA-IISCSP）算法，用于估计四个空间滤波器，其目的是利用受试者内和受试者间的相似性与变异性来增强特征信号的鲁棒性。基于该算法，开发了一个基于迁移学习的分类框架来提高运动想象（MI）脑机接口的性能，其中每个滤波器提取的特征向量通过线性判别分析（LDA）进行降维，并使用支持向量机（SVM）进行分类。在所提出算法在两个MI数据集上进行了性能评估，并与三种最先进的迁移学习算法进行了比较。实验结果表明，对于每类15到50次训练试验，所提出的算法显著优于这些竞争算法，并且可以在保持可接受准确率的同时减少训练数据量，从而促进基于MI的脑机接口的实际应用。
