Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain–computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects’ motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one’s imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.

中文翻译：

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基于运动图像和P300的新型混合脑机接口系统

运动图像（MI）是运动行为的一种心理表征，已广泛用于基于脑电图的脑机接口（BCI）。多项研究证明了基于MI的BCI反馈训练在卒中后康复中的功效。但是，在训练的最早阶段，校准数据通常包含不足的可辨别性，导致反馈不可靠，这可能会降低受试者的动力，甚至会阻碍他们的训练。为了提高MI训练的早期阶段的性能，本研究提出了一种基于MI和P300的新型混合BCI范例。在该范例中，指示对象想象按照在屏幕上显示的所需汉字的闪烁顺序书写汉字。事件相关的失步/同步（ERD / ERS）现象是基于个人的想象力而产生的。同时，每个冲程的闪烁都会唤起P300的电位。此外，提出了一种P300和MI分类的融合方法，其中通过可靠的MI分类来纠正不可靠的P300分类。十二名健康的初次MI受试者参加了这项研究。结果表明，提出的混合BCI范例比单模式BCI范例产生了明显更好的性能。融合方法的识别精度明显高于P300（其中不可靠的P300分类通过可靠的MI分类进行了纠正。十二名健康的初次MI受试者参加了这项研究。结果表明，提出的混合BCI范例比单模式BCI范例产生了明显更好的性能。融合方法的识别精度明显高于P300（其中不可靠的P300分类通过可靠的MI分类进行了纠正。十二名健康的初次MI受试者参加了这项研究。结果表明，提出的混合BCI范例比单模式BCI范例产生了明显更好的性能。融合方法的识别精度明显高于P300（p  <0.05）和MI（p  <0.01）。而且，可以通过融合这两种方式来减少训练数据的大小。