School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Zhejiang Kende Mechanical & Electrical Corporation.
Math Biosci Eng. 2023 Jan;20(3):4560-4573. doi: 10.3934/mbe.2023211. Epub 2022 Dec 26.
The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.
脑电图 (EEG) 信号的非平稳性和个体变异性使得利用脑机接口技术从用户那里获取 EEG 信号具有挑战性。大多数现有的迁移学习方法都是基于离线模式的批量学习，不能很好地适应在线情况下 EEG 信号产生的变化。为了解决这个问题，本文提出了一种基于源域选择的多源在线迁移 EEG 分类算法。通过利用目标域中的少量标记样本，源域选择方法从多个源域中选择与目标数据相似的源域数据。在为每个源域训练一个分类器后，所提出的方法根据预测结果调整每个分类器的权重系数，以避免负迁移问题。该算法应用于两个公开可用的运动想象 EEG 数据集，即 BCI 竞赛 IV 数据集 IIa 和 BNCI 地平线 2020 数据集 2，平均准确率分别为 79.29%和 70.86%，优于几种多源在线迁移算法，证实了该算法的有效性。
