Hilbert-Huang Transformation-based subject-specific time-frequency-space pattern optimization for motor imagery electroencephalogram classification

The advancement of brain-computer interfaces (BCIs) has narrowed the gap between humans and com-puters, allowing intentional interaction by monitoring and translating brain signals in real time. Among BCI approaches, motor imagery electroencephalogram (MI-EEG) systems are popular due to their non-invasiveness, portability, and user-friendly operation without external stimuli. However, MI-EEG classification faces challenges from subject-specific variations in time, frequency, and spatial domains. To overcome this, the paper proposes a novel Hilbert-Huang transformation (HHT)-based method for subject-specific time- frequency-space pattern optimization in MI-EEG classification. The method utilizes a joint time-frequency pattern optimization module and a spatial pattern optimization module for EEG measurements. This efficient process identifies subject-specific dominant time-frequency components and extracts optimal spatial features. The optimized features are fed into a support vector machine (SVM) classifier, resulting in superior performance compared to standard baselines on three open-source datasets. The proposed method achieves 4.1% and 6.3% higher accuracy in 2-class and 4-class classification, respectively. Additionally, it demonstrates remarkable computational efficiency, requiring 70% less training time to achieve the optimal feature space. These improvements in classification accuracy and computational efficiency underscore the practical value of the proposed method for MI-BCI systems.