Multi-source-domain adaptive cross-subject EEG cognitive state evaluation method based on label alignment

• Main Authors: WU ZHENGXUAN, ZHAO YUE, FANG XIN, ZENG HONG, LI XIUFENG, WU JING, DAI GUOJUN, ZHANG ZHENYAN
• Format: Patent
• Language: Chinese; English
• Published: 14.09.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a multi-source-domain self-adaptive cross-subject EEG cognitive state evaluation method based on label alignment. The method comprises the following steps: 1, data acquiring; 2, data preprocessing; 3, a cross-subject EEG cognitive state evaluation method based on the LA-MSDA model. According to the method, a shared common feature extractor and a non-shared sub-feature extractor are used in stages, and tested invariant features and specific features of a source domain sample and a target domain sample are further learned; in consideration of the relationship and similarity between cross-subjects, a method for aligning inter-domain distribution of local and global representation is provided to evaluate the cognitive state of the cross-subjects, and the problem that it is difficult to learn fine-grained class condition information and adapt to decision boundary samples of the cross-subjects is solved. Finally, the problem of individual difference of electroencephalogram signals in the fie