基于 P300 与 ErrP 决策融合的脑-机接口目标检测方法

针对脑-机接口(BCI)技术在目标检测中的应用仍然存在检测准确率受限的问题,提出基于事件相关电位(ERP)中的 P300 与错误相关电位(ErrP)决策融合的新型编解码方法。 BCI 系统编码方面通过目标图像和视觉反馈分别诱发 P300 与 ErrP 特征,解码方面采用单独 P300 特征、单独 ErrP 特征、P300 与 ErrP 特征层融合、P300 与 ErrP 决策层融合这 4 种方案进行目标检 测。 10 名健康受试者 4 种方案进行目标检测的平均结果显示,使用 P300 与 ErrP 决策层融合的平衡正确率最高,达到 80. 03%± 5. 20%,相比单独使用 P300 特征的方法提升了 4. 38%,相比单独使用 ErrP 特征的方法提升了 11. 29%,验证了混合 BCI 技术在 目标检测任务中的可行性。

Brain-computer interface target detection method based on decision fusion of P300 and ErrP

Aiming at the problem of limited detection accuracy in the application of brain-computer interface (BCI) technology in target detection, a new encoding and decoding method based on the decision layer fusion of P300 and error-related potential (ErrP) in event-related potential (ERP) was proposed. In the encoding aspect of the BCI system, the P300 and ErrP features are respectively evoked by the target image and visual feedback. In the decoding aspect, four schemes are used for target detection: individual P300 feature, individual ErrP feature, feature layer fusion of P300 and ErrP, and decision layer fusion of P300 and ErrP. The average results of 10 healthy subjects with four schemes show that the balance accuracy of decision layer fusion of P300 and ErrP is the highest, reaching 80. 03%±5. 20%, which is improved by 4. 38% compared with the method of using individual P300 feature and is improved by 11. 29% compared with the method of using individual ErrP feature. The feasibility of hybrid BCI technology in target detection tasks is verified.