A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface

Fei Wang; Yinxing Wan; Zhuorong Li; Feifei Qi; Jingcong Li

doi:10.3389/fnins.2023.1167125

A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface

Front Neurosci. 2023 Jul 20:17:1167125. doi: 10.3389/fnins.2023.1167125. eCollection 2023.

Authors

Fei Wang^{1

2}, Yinxing Wan¹, Zhuorong Li¹, Feifei Qi^{2

3}, Jingcong Li^{1

2}

Affiliations

¹ School of Software, South China Normal University, Guangzhou, China.
² Pazhou Lab, Guangzhou, China.
³ School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, China.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: EEG; P300; brain computer interface; cross-subject; disorder of consciousness.