Recognition of unilateral lower limb movement based on EEG signals with ERP-PCA analysis

• Published in: Neuroscience letters Vol. 800; p. 137133
• Main Authors: Gu, Lingyun, Jiang, Jiuchuan, Han, Hongfang, Gan, John Q., Wang, Haixian
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 13.03.2023
• Subjects: Brain-Computer Interfaces; Electroencephalography - methods; Evoked Potentials - physiology; Humans; Imagination - physiology; Lower Extremity; Lower limb; Motor execution; Motor imagery; Movement - physiology; Principal components analysis; Upper Extremity; Motor execution; Lower limb; Principal components analysis; Motor imagery
• ISSN: 0304-3940; 1872-7972; 1872-7972
• DOI: 10.1016/j.neulet.2023.137133

•We confirmed a high degree of similarity of lower limbs between motor imagery and motor execution.•The distinguished temporal-spatial distribution has a profound effect on the lateralization of the lower limb movements.•The ERP-based classification model can be a potential new classification model for brain-computer interface systems on lower limb movements. It has been confirmed that motor imagery (MI) and motor execution (ME) share a subset of mechanisms underlying motor cognition. In contrast to the well-studied laterality of upper limb movement, the laterality hypothesis of lower limb movement also exists, but it needs to be characterized by further investigation. This study used electroencephalographic (EEG) recordings of 27 subjects to compare the effects of bilateral lower limb movement in the MI and ME paradigms. Event-related potential (ERP) recorded was decomposed into meaningful and useful representatives of the electrophysiological components, such as N100 and P300. Principal components analysis (PCA) was used to trace the characteristics of ERP components temporally and spatially, respectively. The hypothesis of this study is that the functional opposition of unilateral lower limbs of MI and ME should be reflected in the different alterations of the spatial distribution of lateralized activity. Meanwhile, the significant ERP-PCA components of the EEG signals as identifiable feature sets were applied with support vector machine to identify left and right lower limb movement tasks. The average classification accuracy over all subjects is up to 61.85% for MI and 62.94% for ME. The proportion of subjects with significant results are 51.85% for MI and 59.26% for ME, respectively. Therefore, a potential new classification model for lower limb movement can be applied on brain computer interface (BCI) systems in the future.