为了解决不同时间采集的运动想象脑电数据之间存在的分布差异，避免跨时段使用前长时间的重校准步骤，提出了一种基于判别迁移特征学习(discriminative transfer feature learning，DTFL)的运动想象分类方法。DTFL通过联合匹配源域和目标域之间的边缘分布和类条件分布来减少域间的差异，同时最大化类间距离和最小化类内距离来保留类判别信息，从而提升对运动想象的分类性能。基于DTFL的运动想象分类方法无需目标域脑电样本的类别信息，可以有效避免长时间的校准。在脑机接口竞赛数据集上的实验结果表明，DTFL显著优于其他迁移学习方法，有效缓解跨域分布的不一致性，提高了运动想象的分类正确率。
Discriminative transfer feature for motor imagery brain-computer interfaces
To address the cross-sessions variability of motor imagery electroencephalogram (EEG) and eliminate the need for lengthy recalibration step, this study proposes a motor imagery classification method based on discriminative transfer feature learning (DTFL). DTFL aims to reduce domain differences by jointly matching the marginal distribution and class conditional distribution of both domains. Simultaneously, DTFL maximizes inter-class dispersion and minimizes intra-class scatter, preserving class discrimination information and improving classification performance. This method does not require class information for EEG samples in the target domain, effectively avoiding the need for long-term calibration. Experimental results on brain-computer interface competition datasets demonstrate that, compared with some transfer learning methods, the proposed DTFL mitigates cross-session variability and improves the classification accuracy of motor imagery EEG.
万方数据
维普
