Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation.
10.7507/1001-5515.202411035
- Author:
Lincong PAN
1
;
Xinwei SUN
1
;
Kun WANG
1
;
Yupei CAO
1
;
Minpeng XU
1
;
Dong MING
1
Author Information
1. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Cross-session;
Electroencephalography;
Machine learning;
Motor imagery
- MeSH:
Electroencephalography/methods*;
Brain-Computer Interfaces;
Humans;
Imagination/physiology*;
Signal Processing, Computer-Assisted;
Movement/physiology*;
Signal-To-Noise Ratio;
Deep Learning;
Algorithms
- From:
Journal of Biomedical Engineering
2025;42(2):272-279
- CountryChina
- Language:Chinese
-
Abstract:
Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
