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

• Published in: Measurement : journal of the International Measurement Confederation Vol. 223; p. 113673
• Main Authors: Liu, Xiaolin, Sun, Ying, Zheng, Dezhi, Na, Rui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Brain computer interface; Electroencephalogram; Hilbert–Huang transformation; Motor imagery classification; Time–frequency-space analysis; Hilbert–Huang transformation; Brain computer interface; Time–frequency-space analysis; Electroencephalogram; Motor imagery classification
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2023.113673

The advancement of brain–computer interfaces (BCIs) has narrowed the gap between humans and computers, 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. •Efficient HHT-based method for precise subject-specific TF component identification.•Spatial EEG filtering with focus on dominant time-frequency components.•Outperforms baselines, achieving 4.1% and 6.3% higher accuracy on open datasets.