Objective. For nonstationarity of neural recordings, daily retraining is required in the decoder calibration of intracortical brain-machine interfaces (iBMIs). Domain adaptation (DA) has started to be applied in iBMIs to solve the problem of daily retraining by taking advantage of historical data. However, previous DA studies used only a single source domain, which might lead to performance instability. In this study, we proposed a multi-source DA algorithm, by fully utilizing the historical data, to achieve a better and more robust decoding performance while reducing the decoder calibration time. Approach. The neural signals were recorded from two rhesus macaques using intracortical electrodes to decode the reaching and grasping movements. A principal component analysis (PCA)-based multi-source domain adaptation (PMDA) algorithm was proposed to apply the feature transfer to diminish the disparities between the target domain and each source domain. Moreover, the multiple weighted sub-classifiers based on multi-source domain data and small current sample set were constructed to accomplish the decoding. Main results. Our algorithm was able to make use of the multi-source domain data and achieve better and more robust decoding performance compared with other methods. Only a small current sample set was needed by our algorithm in order for the decoder calibration time to be effectively reduced. Significance. (1) The idea of the multi-source DA was introduced into the iBMIs to solve the problem of time consumption in the daily decoder retraining. (2) Instead of using only single-source domain data in the previous study, our algorithm made use of multi-day historical data, resulting in better and more robust decoding performance. (3) Our algorithm could be accomplished with only a small current sample set, and it can effectively reduce the decoder calibration time, which is important for further clinical applications.

客观的。对于神经记录的非平稳性，在皮质内脑机接口 (iBMIs) 的解码器校准中需要每天进行再训练。领域适应 (DA) 已开始应用于 iBMI，以利用历史数据解决日常再培训问题。但是，以前的 DA 研究仅使用单个源域，这可能会导致性能不稳定。在本研究中，我们提出了一种多源 DA 算法，充分利用历史数据，在减少解码器校准时间的同时获得更好、更鲁棒的解码性能。方法。使用皮质内电极从两只恒河猴记录神经信号以解码到达和抓握运动。提出了一种基于主成分分析（PCA）的多源域自适应（PMDA）算法来应用特征迁移来减少目标域和每个源域之间的差异。此外，构建了基于多源域数据和小电流样本集的多个加权子分类器来完成解码。主要结果。与其他方法相比，我们的算法能够利用多源域数据，实现更好、更鲁棒的解码性能。我们的算法只需要一个小的当前样本集，就可以有效地减少解码器校准时间。意义。(1) iBMIs引入多源DA的思想，解决日常解码器再训练的时间消耗问题。（2）我们的算法不是在之前的研究中只使用单源域数据，而是利用了多天的历史数据，从而获得了更好、更鲁棒的解码性能。（3）我们的算法只需要少量的当前样本集就可以完成，它可以有效地减少解码器的校准时间，这对于进一步的临床应用很重要。