Cross-Subject DD: A Cross-Subject Brain-Computer Interface Algorithm

• URL: http://arxiv.org/abs/2507.05268v1
• Date: Wed, 02 Jul 2025 09:53:15 GMT
• Title: Cross-Subject DD: A Cross-Subject Brain-Computer Interface Algorithm
• Authors: Xiaoyuan Li, Xinru Xue, Bohan Zhang, Ye Sun, Shoushuo Xi, Gang Liu,
• Abstract summary: Brain-computer interface (BCI) based on motor imagery enables direct control of external devices by decoding the electroencephalogram (EEG) generated in the brain during imagined movements.<n>existing BCI models exhibit poor adaptability across subjects, limiting their generalizability and widespread application.<n>This paper proposes a cross-subject BCI algorithm named Cross-Subject DD (CSDD), which constructs a universal BCI model by extracting common features across subjects.
• Score: 6.612070142961214
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Brain-computer interface (BCI) based on motor imagery (MI) enables direct control of external devices by decoding the electroencephalogram (EEG) generated in the brain during imagined movements. However, due to inter-individual variability in brain activity, existing BCI models exhibit poor adaptability across subjects, thereby limiting their generalizability and widespread application. To address this issue, this paper proposes a cross-subject BCI algorithm named Cross-Subject DD (CSDD), which constructs a universal BCI model by extracting common features across subjects. The specific methods include: 1) training personalized models for each subject; 2) transforming personalized models into relation spectrums; 3) identifying common features through statistical analysis; and 4) constructing a cross-subject universal model based on common features. The experiments utilized the BCIC IV 2a dataset, involving nine subjects. Eight of these subjects were selected for training and extracing the common features, and the cross-subject decoding performance of the model was validated on the remaining subject. The results demonstrate that, compared with existing similar methods, our approach achieves a 3.28% improvement in performance. This paper introduces for the first time a novel method for extracting pure common features and constructing a universal cross-subject BCI model, thereby facilitating broader applications of BCI technology.

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