BACKGROUND Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG-fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users. RESULTS Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm. CONCLUSIONS Data collected using the MI paradigm show better generalization across subjects.

中文翻译：

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一种在混合EEG-fTCD脑机接口中减少校准时间的概率方法。

背景技术通常，脑计算机接口（BCI）在使用前需要进行校准以确保有效的性能。因此，每个BCI用户必须参加一定数量的校准会话才能使用该系统。但是，这种校准要求可能很难满足，尤其是对于残疾患者。在本文中，我们介绍了一种概率转移学习方法，以减少使用运动图像（MI）和闪烁的思维旋转（MR）/单词生成（WG）范式设计的EEG-fTCD混合BCI的校准要求。所提出的方法识别从先前BCI用户到从当前BCI用户收集的小型培训数据集的顶级相似数据集，并使用这些数据集来扩充当前BCI用户的培训数据。为了达到这个目的 使用支持向量机将每个试验的EEG和fTCD特征向量投影到标量分数中。使用每个班级的分数分别学习EEG和fTCD班级的条件分布。Bhattacharyya距离用于识别使用当前BCI用户的训练试验获得的班级条件分布与使用先前用户的试验获得的班级条件分布之间的相似性。结果实验结果表明，对于MI和闪烁的MR / WG范例，使用建议的转移学习方法获得的性能要优于没有转移学习的性能。特别是，发现MI范例的校准要求至少可以降低60.43％，而MR / WG范例的校准要求最多可以降低17.31％。