Wang Fei, Wan Yinxing, Li Zhuorong, Qi Feifei, Li Jingcong
School of Software, South China Normal University, Guangzhou, China.
Pazhou Lab, Guangzhou, China.
Front Neurosci. 2023 Jul 20;17:1167125. doi: 10.3389/fnins.2023.1167125. eCollection 2023.
Brain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients' EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient's own data and performs poorly.
In this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients' P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data.
The results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment.
These results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients.
脑机接口（BCI）技术可能为一些意识障碍（DOC）患者提供一种新的交流方式，它能直接连接大脑与外部设备。然而，DOC患者的脑电图与正常人有显著差异且难以采集，目前的解码算法仅基于少量患者自身数据进行训练，效果不佳。
在本研究中，提出了一种基于域适应的名为WD-ADSTCN的解码算法，以提高DOC患者P300信号的检测能力。我们使用瓦瑟斯坦距离对正常人群数据进行过滤，以增加训练数据。此外，采用对抗方法来解决正常数据与患者数据之间的差异。
结果显示，在DOC患者的跨受试者P300检测中，11名患者中有7名平均准确率超过70%。此外，实验三个月后，他们的临床诊断发生了变化，CRS-R评分有所提高。
这些结果表明，所提出的方法可用于DOC患者的P300 BCI系统，这对这些患者的临床诊断和预后具有重要意义。
