.png){kind=icon}
.png){kind=icon}
Return
Exploring multi-scale time group for common spatial pattern feature based motor imagery EEG classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2025.108591.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Feature extraction is a pivotal challenge in brain–computer interface (BCI) systems that utilize motor imagery (MI). Discriminative features are crucial for enhancing the classification accuracy of MI tasks within MI-BCI systems. The common spatial pattern (CSP) method has been extensively employed for extracting band-power features in this context. Prior research indicates that the efficacy of the CSP algorithm can be significantly improved by identifying optimal filter bands or selecting appropriate time windows. Most existing methods adopt a fixed-size time window, which may overlook EEG patterns occurring in windows of varying sizes. Furthermore, current approaches have not explored the potential of optimizing the filtering band for different time windows, thereby potentially compromising the effectiveness of the CSP algorithm. In this paper, we introduce a novel method called multi-scale time group common spatial pattern (MTGCSP), which aims to optimize both the time window for MI tasks based on a multi-scale sliding time window preprocessing strategy and the filtering band for each time window at a specific scale. Specifically, the EEG signals are initially filtered across multiple sub-bands. Subsequently, we use the multi-scale sliding time window preprocessing strategy to segment the filtered spectral signals into multiple subsequences of varying lengths using the multi-scale sliding window. To extract robust CSP features from the multi-scale sliding time window subsequences, we propose a sparse joint optimization objective function that incorporates sparse group constraints. The resulting feature subset is then fed into a support vector machine (SVM) classifier with a linear kernel to perform MI-EEG classification tasks. Experimental results from three public datasets demonstrate that the MTGCSP method outperforms other state-of-the-art techniques.
Keywords:
feature extraction
brain-computer interface
motor imagery
common spatial pattern
multi-scale time window
Journal
IF:
4.9
Papers:
9.8K
Citations:
2.4W